Multi-stage cooperative fault detection method and system based on intelligent air switch

Abstract

The invention relates to the technical field of electrical safety, and discloses a multistage cooperative fault detection method and system based on an intelligent air switch, and the method comprises the steps: intelligent air switch terminals disposed at all levels of a power distribution network are used for synchronously collecting a multi-physical-quantity time sequence signal; the intelligent air switch terminal calculates the dynamic correlation entropy of the collected current signal and executes local fault diagnosis according to the dynamic correlation entropy; the terminals carry out cooperative communication, and dynamic correlation entropy is collected to construct an entropy migration spectrum so as to carry out cooperative traceability on faults; and finally, integrating local diagnosis and collaborative traceability results, and executing corresponding hierarchical collaborative protection actions. The system comprises an acquisition module; an entropy calculation module; a local diagnosis module; a communication module; a collaborative traceability module; and a protection execution module. According to the invention, through multi-stage cooperative fault detection of the intelligent air switch, accurate identification, rapid response and state early warning of hidden faults are realized, and the safety and reliability of a power distribution system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical safety, in particular to a multi-level cooperative fault detection method and system based on intelligent air switch. BACKGROUND

[0002] In modern power systems, air switch (referred to as air switch) is the most widely used basic protection electrical appliance, which undertakes the core function of cutting off overload and short-circuit current, protecting the safety of lines and equipment. With the promotion of smart grid construction and the popularity of industrial internet of things, the access of a large number of distributed new energy and high-frequency power electronic devices makes the operation environment and fault mode of low-voltage distribution system increasingly complex, which also puts forward higher technical requirements for the accuracy, rapidity and foresight of fault protection.

[0003] In the prior art, the traditional air switch mainly relies on the built-in thermal magnetic tripping mechanism to realize protection. The mechanism uses the principle of thermal bending of bimetallic strip due to overcurrent to deal with long-term overload, or uses the principle of strong magnetic attraction of electromagnet due to short-circuit large current to cut off the circuit. This protection mode based on a single physical quantity of current amplitude for passive response plays an important role in the past simple radial power supply network.

[0004] However, in the current complex power grid environment, the inherent defects of this traditional protection mechanism are increasingly prominent. First of all, this mechanism relies on a single current threshold as a criterion, and for hidden faults such as series arc and poor contact that do not cause significant overcurrent, its detection sensitivity is insufficient, it is difficult to identify effectively, and it is easily disturbed by harmonic current to cause misoperation or refusal to operate. First of all, the mechanism relies on mechanical structure action, and its response speed is usually tens of milliseconds, which lags behind the formation time of the first peak of short-circuit current (usually within 10 milliseconds), so it cannot effectively suppress the energy impact in the early stage of fault. First of all, due to the lack of sensing function of temperature and other physical quantities, the mechanism cannot monitor the gradual degradation process caused by contact aging, virtual connection and other factors, and does not have the ability of state early warning, which may lead to the existence of electrical fire hazards.

[0005] Therefore, the present application proposes a multi-level cooperative fault detection method and system based on intelligent air switch to solve the problems of the prior art. SUMMARY

[0006] The purpose of the present application is to provide a multi-level cooperative fault detection method and system based on intelligent air switch, which solves the problems that the existing air switch has a single protection criterion, slow response speed and lacks a cooperative mechanism, so it is difficult to effectively identify hidden faults such as series arc, cannot quickly cut off the early fault current, and does not have the ability of state early warning.

[0007] To achieve the above object, the present application is implemented by the following technical solutions: The first aspect of the present application provides a multi-level cooperative fault detection method based on intelligent air switch, which comprises: Step one: synchronously collecting multi-physical quantity time series signals by the intelligent air switch terminals deployed at each level of the power distribution network.Step two: reconstructing phase space by the intelligent air switch terminals based on the collected current time series, and calculating the dynamic correlation entropy reflecting the dynamic behavior of the power circuit.Step three: performing local fault diagnosis by the intelligent air switch terminals based on the dynamic correlation entropy.Step four: performing cooperative communication between the intelligent air switch terminals, constructing entropy migration spectrum based on the dynamic correlation entropy of each air switch terminal, and cooperatively tracing the fault according to the entropy migration spectrum.Step five: performing hierarchical protection actions according to the results of the local fault diagnosis and the cooperative tracing.

[0008] Preferably, in step one, after completing time synchronization by network time protocol or GPS module, the intelligent air switch terminals deployed at each level of the power distribution network collect the multi-physical quantity time series signals, wherein the multi-physical quantity time series signals include current time series, voltage time series, contact temperature sequence and bus temperature sequence.

[0009] In one specific embodiment, the step of reconstructing phase space by the intelligent air switch terminals based on the collected current time series and calculating the dynamic correlation entropy in step two comprises: reconstructing the current time series I(i) into a state vector Y in m-dimensional phase space by time delay embedding method i to construct an attractor capable of representing the dynamic behavior of the power circuit: Y i =[I(i),I(i+τ),…,I(i+(m-1)τ)]; wherein I(i) is the current sampling value at discrete time index i, m is the embedding dimension, and τ is the time delay. calculating the correlation integral based on the state vector Y i , and then determining the dynamic correlation entropy as a quantitative indicator of the complexity of the dynamic behavior of the power circuit according to the rate of change of the correlation integral with the embedding dimension m.

[0010] Preferably, the step of performing local fault diagnosis by the intelligent air switch terminals based on the dynamic correlation entropy in step three comprises performing at least one of the following diagnostic logics: 1) calculating the first-order difference of the dynamic correlation entropy, and determining short circuit or parallel arc fault when the first-order difference exceeds a first preset threshold; 2) judging the amplitude of the dynamic correlation entropy, and determining that it is a series arc fault when the amplitude is continuously higher than a second preset threshold value; 3) judging in combination with the dynamic correlation entropy and the contact temperature sequence and the bus temperature sequence obtained in the synchronous acquisition of the multi-physical quantity time sequence signal, obtaining a differential temperature by calculating the difference between the contact temperature sequence and the bus temperature sequence, and determining that it is a gradual fault when both the dynamic correlation entropy and the differential temperature show a continuously increasing trend.

[0011] In one specific embodiment, the step of constructing the entropy migration spectrum in step four includes: When any of the intelligent air switch terminals detects a preset change in its dynamic correlation entropy, the intelligent air switch terminal generates an entropy migration event, and broadcasts event information including a timestamp of the event and an amplitude of the entropy to other intelligent air switch terminals in the power distribution network through the communication module of the intelligent air switch terminal; The entropy migration events caused by the same physical disturbance are received and collected by the superior intelligent air switch terminal to form the entropy migration spectrum.

[0012] Further, the step of cooperatively tracing the fault according to the entropy migration spectrum in step four includes: Based on the timestamp and the amplitude of the entropy of each entropy migration event in the entropy migration spectrum, a time delay spectrum and an attenuation factor spectrum are calculated; And according to the combined characteristics of the time delay spectrum and the attenuation factor spectrum, the fault type is matched and determined from a preset fault feature spectrum library.

[0013] Specifically, the calculation method of the attenuation factor spectrum is: In the formula, A ems is the attenuation factor spectrum; K 2,s is the amplitude of the entropy value of the fault source node; K 2,s-1 to K 2,1 are the amplitudes of the entropy values detected by the intelligent air switch terminals at each level from the fault source node to the upper level in the direction of power transmission, respectively; And according to the characteristics of the attenuation factor spectrum, the fault type is determined: When the attenuation factor spectrum shows a rapid attenuation characteristic, it is determined to be a series fault; When the attenuation factor spectrum shows a small attenuation characteristic, it is determined to be a parallel fault.

[0014] In one specific embodiment, the step of performing a hierarchical protection action in step five includes: When the result of the local fault diagnosis is a short circuit or parallel arc fault, the intelligent circuit breaker terminal that made the diagnosis directly drives the power electronic switch built into the intelligent circuit breaker terminal to quickly disconnect the circuit. When the result of the collaborative tracing is a parallel fault, the upper-level intelligent circuit breaker terminal that has collected the entropy migration spectrum sends a pre-blocking command to the higher-level intelligent circuit breaker terminal to which the upper-level intelligent circuit breaker terminal belongs, so that the power electronic switch of the higher-level intelligent circuit breaker terminal enters the standby state and performs collaborative backup protection. Furthermore, after the power electronic switch performs the rapid disconnection, the intelligent circuit breaker terminal that performed the disconnection action drives the electromagnetic tripping mechanism built into the intelligent circuit breaker terminal after a delay to complete the final physical disconnection.

[0015] Preferably, the method further includes: After the graded protection action is performed, or when the fault is determined to be a gradual fault based on the local fault diagnosis, the fault-related data, including the local fault diagnosis result, the collaborative tracing result, the entropy migration spectrum, and the multi-physical quantity time series signal, are uploaded to the cloud platform for archiving.

[0016] The second aspect of the present invention provides a multi-level collaborative fault detection system based on intelligent circuit breakers. The system includes multiple intelligent circuit breaker terminals deployed at various levels of the power distribution network, and each of the intelligent circuit breaker terminals includes a functional module for synchronously acquiring time-series signals of multiple physical quantities. The entropy calculation module is used to reconstruct the phase space of the current time series acquired by the acquisition module and calculate the dynamic correlation entropy that reflects the dynamic behavior of the power circuit. The local diagnostic module is used to perform local fault diagnosis based on the dynamic correlation entropy; The communication module enables the smart circuit breaker terminal to communicate collaboratively with other smart circuit breaker terminals. The collaborative tracing module is used to construct an entropy migration spectrum based on the dynamic correlation entropy of each terminal through the collaborative communication, and to perform collaborative tracing of faults according to the entropy migration spectrum; The protection execution module is used to perform hierarchical protection actions based on the diagnostic results of the local diagnostic module and the traceability results of the collaborative traceability module.

[0017] In summary, the present invention has at least one of the following beneficial technical effects: 1.The present application can accurately capture fault characteristics from the dimension of signal complexity by introducing dynamic correlation entropy as the core indicator of fault diagnosis, reconstructing the phase space and conducting quantitative analysis on the collected current time series. Compared with the traditional method which only relies on current amplitude threshold, the present application can effectively identify and distinguish hidden non-overcurrent faults such as series arc and poor contact from normal load fluctuation and harmonic interference, fundamentally solving the problem of fault missed detection and misoperation caused by single criterion of traditional circuit breaker, and significantly improving the accuracy and breadth of fault identification.

[0018] 2.The present application realizes rapid response and accurate isolation of faults by constructing entropy migration spectrum for multi-level collaborative tracing and combining with the protection action of power electronic switch. Using the group perception ability of multiple intelligent air break terminals, the present application can accurately lock the fault source and determine its fundamental type, and then guide the power electronic switch to cut off at microsecond level. This method not only effectively suppresses the impact of the first peak of short-circuit current, but also avoids fault expansion and step-out tripping. Compared with the traditional mechanical release mechanism which responds slowly and has no selective protection, the safety and power supply reliability of the system are greatly enhanced.

[0019] 3.The present application can make the circuit breaker have forward-looking warning ability by fusing multi-physical quantity time series such as contact temperature and using dynamic correlation entropy to quantify the subtle changes of system state. By continuously tracking and analyzing the gradual change trend of entropy value and the abnormal rise of temperature, the present application can give early warning before the gradual faults such as equipment insulation aging and virtual contact of contact point develop into serious electrical fire. This makes the intelligent air break from a pure post-fault protection device to an intelligent terminal that can monitor the health status of the system and predict risks, significantly improving the operation efficiency and intrinsic safety level of the distribution system. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 It is a flow chart of the multi-level collaborative fault detection method of the present application; Figure 2 It is a schematic diagram of the system architecture of the present application; Figure 3 It is a schematic diagram of the hardware composition of the intelligent air break terminal of the present application; Figure 4 It is a functional module block diagram of the intelligent air break terminal of the present application.

[0021] Among them, 100, intelligent air break terminal; 101, microcontroller unit; 102, multi-physical quantity sensing unit; 103, protection execution unit; 104, communication unit; 105, storage unit; 110, acquisition module; 120, entropy calculation module; 130, local diagnosis module; 140, communication module; 150, collaborative tracing module; 160, protection execution module; 170, data management module; 200, cloud platform. DETAILED DESCRIPTION

[0022] In order to make the objects, technical solutions and advantages of the present application clearer, the following will further describe the present application with reference to the accompanying drawings. It should be understood that the specific embodiments herein are only used to explain the present application and not to limit the present application.

[0023] The specific embodiments will be described below with reference to the accompanying drawings. Figure 1 - the accompanying drawings Figure 4 The present application will be further described in detail.

[0024] Reference will be made to the accompanying drawings Figures 2-4 The present application provides a multi-level collaborative fault detection system based on intelligent air switch. The system is applied to a power distribution network, for example, a three-level power distribution structure including a general power distribution box, a distribution power distribution box and a terminal user power distribution box. The system includes a plurality of intelligent air switch terminals 100 deployed at each level of the power distribution network, and an optional cloud platform 200 for data archiving and analysis. Each intelligent air switch terminal 100 exchanges data with each other and with the cloud platform 200 through a communication network.

[0025] Each intelligent air switch terminal 100 in the embodiment of the present application is an independent physical device, which integrates hardware circuit and software module running thereon to perform fault detection and protection functions. The hardware structure of the intelligent air switch terminal 100 includes: a microcontroller unit (MCU) 101, a multi-physical quantity sensing unit 102, a protection execution unit 103, a communication unit 104 and a storage unit 105. The MCU 101 serves as a control core and is electrically connected and data-interacted with the sensing unit 102, the protection execution unit 103, the communication unit 104 and the storage unit 105.

[0026] The storage unit 105 stores program instructions, and the MCU 101 realizes the coordinated work of multiple functional modules by executing these program instructions.

[0027] The intelligent air switch terminal 100 specifically includes the following functional modules: The acquisition module 110 is used to control the multi-physical quantity sensing unit 102 to synchronously acquire the physical quantities of the circuit. The multi-physical quantity sensing unit 102 includes a current sensor, a voltage sampling circuit and a temperature sensor. The acquisition module 110 synchronously acquires the current time series, the voltage time series and the temperature series installed on the circuit breaker contact and the busbar at a preset sampling frequency.

[0028] The entropy calculation module 120 is configured to receive the current time series acquired by the acquisition module 110. The module performs a phase space reconstruction operation on the current time series, and calculates the correlation integral based on the reconstructed state vector, and further determines the dynamic correlation entropy. The dynamic correlation entropy is used to quantify the complexity of the dynamic behavior of the current power circuit.

[0029] The local diagnosis module 130 is configured to receive and analyze the dynamic correlation entropy output by the entropy calculation module 120. The module determines the fault type of the local circuit by calculating the first-order difference of the dynamic correlation entropy, judging its amplitude, and combining the temperature sequence acquired by the acquisition module 110. The determination result includes short circuit, parallel arc fault, series arc fault or gradual fault.

[0030] The communication module 140 is composed of the communication unit 104 and its driver. The module is configured to establish a communication link between the intelligent air switch terminal 100 and other intelligent air switch terminals 100, and between the intelligent air switch terminal 100 and the cloud platform 200. It is responsible for broadcasting the entropy migration event, issuing and receiving the pre-blocking instruction, and uploading the fault-related data.

[0031] The collaborative tracing module 150 is configured to receive the entropy migration events broadcast by other intelligent air switch terminals in the power distribution network through the communication module 140. The module collects a series of entropy migration events from the same physical disturbance, constructs an entropy migration spectrum, and calculates the time delay spectrum and the attenuation factor spectrum based on the spectrum. Finally, by matching the preset fault characteristic spectrum library, the source location and specific type of the fault are determined.

[0032] The protection execution module 160 is configured to issue control instructions to the protection execution unit 103 according to the diagnosis result of the local diagnosis module 130 or the tracing result of the collaborative tracing module 150. The protection execution unit 103 internally includes a power electronic switch and an electromagnetic tripping mechanism. The module can drive the power electronic switch to perform microsecond-level fast cutting, or drive the electromagnetic tripping mechanism to perform millisecond-level physical breaking.

[0033] In addition, the intelligent air switch terminal 100 in the embodiment also includes a data management module 170. The module is configured to package the fault data including the local fault diagnosis result, the collaborative tracing result, the entropy migration spectrum and the multi-physical quantity time sequence signal after the end of the fault handling process, and upload it to the cloud platform 200 for archiving through the communication module 140.

[0034] Referring to the accompanying drawings Figure 1 The method is cooperatively performed by a plurality of intelligent air switch terminals 100 deployed in the power distribution network, and can specifically include the following steps: S100, the intelligent air switch terminals deployed at each level of the power distribution network perform synchronous acquisition of multi-physical quantity time sequence signals.

[0035] S200, the intelligent air switch terminal reconstructs the current time sequence collected to obtain a dynamic correlation entropy reflecting the dynamic behavior of the power circuit.

[0036] S300, the intelligent air switch terminal performs local fault diagnosis based on the dynamic correlation entropy.

[0037] S400, the intelligent air switch terminals perform collaborative communication, build an entropy migration spectrum based on the dynamic correlation entropy of each air switch terminal, and collaboratively trace the fault according to the entropy migration spectrum.

[0038] S500, according to the results of local fault diagnosis and collaborative tracing, perform hierarchical protection actions.

[0039] The method further comprises: S600, fault data archiving. After performing the hierarchical protection actions, or when it is determined that the fault is gradual, the data management module 170 packages the local fault diagnosis results, the collaborative tracing results, the entropy migration spectrum, and the multi-physical quantity time sequence signals related to the event, and uploads them to the cloud platform 200 for archiving through the communication module 140.

[0040] The multi-level collaborative fault detection method of the embodiment of the application starts with the synchronous acquisition of multi-physical quantity time sequence signals in the intelligent air switch terminal 100 deployed at each level of the power distribution network (S100). This step is the data basis for all subsequent fault diagnosis and collaborative tracing, and the accuracy and synchronization of its execution directly determine the performance of the entire system. This step is performed by the acquisition module 110 inside each intelligent air switch terminal 100, and the communication module 140 provides time synchronization support. The specific implementation process is as follows.

[0041] First, to ensure that the data collected by multiple intelligent air switch terminals 100 distributed in different physical locations have a unified time reference, the acquisition module 110 will trigger the communication module 140 to perform high-precision time synchronization before data acquisition. In one embodiment, the communication module 140 periodically communicates with the upper gateway deployed in the local network or the time server on the public Internet through the Network Time Protocol (NTP) to obtain standard time stamps and calibrate the local real-time clock (RTC) of the internal microcontroller (MCU) 101, ensuring that the time error is controlled within milliseconds. In scenarios with higher requirements for time accuracy, the communication module 104 can integrate a Global Positioning System (GPS) receiver. By receiving GPS satellite signals, especially the PPS (Pulse Per Second) signal contained therein, the synchronization error of the local clock can be reduced to the microsecond level. High-precision time synchronization is a prerequisite for accurately locating the fault source by calculating the time delay of the entropy migration event in the subsequent collaborative tracing step S400.

[0042] After the time synchronization is completed, the acquisition module 110 synchronously samples the multiple key physical quantities through the multi-physical quantity sensing unit 102 at a fixed, preset sampling frequency (for example, 6.4 kHz). The multi-physical quantity time sequence specifically includes: a current time sequence, a voltage time sequence, a contact temperature sequence, and a busbar temperature sequence. Synchronous sampling is adopted to ensure that the four kinds of data obtained at any sampling moment accurately correspond to the circuit state at the same time.

[0043] Specifically, for the acquisition of the current time sequence, the multi-physical quantity sensing unit 102 uses a high-bandwidth Hall effect sensor or a high-precision shunt resistor to continuously measure the current flowing through the main circuit of the circuit breaker, and the output analog signal is converted into a digital sequence through an analog-to-digital converter (ADC). The current time sequence is the core input for calculating the dynamic correlation entropy.

[0044] For the acquisition of the voltage time sequence, a resistor voltage dividing circuit is used to proportionally reduce the line voltage of 220V or 380V to the safe input range (for example, 0-3.3V) of the internal ADC of the microcontroller (MCU) 101, so as to realize real-time sampling of the voltage waveform. The voltage sequence is mainly used for auxiliary judgment, such as distinguishing between voltage sag on the power grid side and load failure on the user side.

[0045] For the acquisition of the contact temperature sequence and the busbar temperature sequence, high-precision negative temperature coefficient (NTC) thermistors or PT100 platinum thermistors are tightly attached to the vicinity of the moving and static contacts inside the intelligent air switch terminal 100 and the main incoming line busbar, respectively, through a heat-conducting medium. The acquisition module 110 converts the corresponding temperature by measuring the resistance value or the voltage division value. The acquisition of the contact temperature is to monitor the abnormal temperature rise caused by arc ablation or contact pressure drop, and the acquisition of the busbar temperature is to provide a reference. By calculating the difference between the contact temperature and the busbar temperature, the interference of the overall environmental temperature rise caused by the change of the load current can be excluded, so as to realize accurate diagnosis of gradual faults.

[0046] The acquisition module 110 binds the current, voltage, contact temperature, and busbar temperature four data points obtained in each round of synchronous sampling with the current high-precision time stamp to form a data frame, and stores it in the buffer area of the storage unit 105 for subsequent calling by the entropy calculation module 120 and the local diagnosis module 130.

[0047] After the collection of the multi-physical quantity time sequence signal is completed in step S100, the process enters step S200, and the entropy calculation module 120 disposed in each intelligent air switch terminal 100 processes the collected current time sequence to calculate the dynamic correlation entropy. This step is the core of the fault diagnosis method of the present application, and the purpose is to convert the one-dimensional and seemingly random current signal into an index that can accurately quantify the complexity of the dynamic behavior of the power system. The specific implementation process includes the following two stages.

[0048] Reconstruction of the phase space of the current time sequence, the entropy calculation module 120 first reads the current time sequence collected and stored by the collection module 110 from the storage unit 105. The reason for choosing the current time sequence as the analysis object is that the current signal directly reflects the behavior of the load and the state of the line, and is most sensitive to disturbances such as short circuits, arcs, and poor contact.

[0049] In order to reveal the system dynamics characteristics hidden behind the one-dimensional time sequence, the entropy calculation module 120 uses the time delay embedding method to reconstruct the phase space of the current time sequence. This method expands the one-dimensional time sequence into a high-dimensional phase space to form an attractor that can represent the dynamic behavior of the power circuit. The reconstruction process is performed by the following formula: i Y(i) = [I(i), I(i+τ), …, I(i+(m-1)τ)]; In the formula, I(i) refers to the current sampling value at discrete time index i; m is the embedding dimension; τ is the time delay, which refers to the time interval between consecutive components when constructing the state vector; Y i is a state vector in the reconstructed m-dimensional phase space at time index i, and the set of all state vectors {Y i} constitutes the phase space attractor of the system.

[0050] The selection of the embedding dimension m and the time delay τ is crucial to the effectiveness of the phase space reconstruction. In this embodiment, the two parameters are determined by the following method: the time delay τ is determined by calculating the autocorrelation function or the average mutual information function of the current time sequence, and the time delay corresponding to the first minimum point of the function is usually selected to ensure that the linear independence or nonlinear correlation of the components in the state vector is minimized. The embedding dimension m is determined by the False Nearest Neighbors (FNN) method, which increases the value of m and calculates the proportion of the false nearest neighbors in the phase space. When the proportion first drops to zero or a sufficiently small threshold, the corresponding m value is the optimal embedding dimension, which ensures that the attractor is fully expanded in the reconstructed space without false overlap.

[0051] Calculation of dynamic correlation entropy, after the phase space reconstruction is completed and the set of state vectors {Y iAfterwards, the entropy calculation module 120 performs subsequent calculation. Specifically, the module calculates the correlation integral C i The correlation integral is calculated, and then the dynamic correlation entropy is determined according to the rate of change of the correlation integral with the embedding dimension m, as a quantitative index of the complexity of the dynamic behavior of the power circuit.

[0052] First, the correlation integral C m (r) is calculated. For a given embedding dimension m and a small distance radius r, the correlation integral C m (r) is defined as the probability that the distance between all pairs of state vectors (Y i ,Y j ) in the m-dimensional phase space is less than r. Its calculation formula is: In the formula, N is the total number of state vectors; ‖·‖ represents the distance between two vectors, usually using the Euclidean norm; and Θ(·) is the Heaviside step function, whose value is 1 when the independent variable is greater than or equal to 0, and 0 otherwise.

[0053] Subsequently, the dynamic correlation entropy, i.e., the K2 entropy, referred to in the present application, is determined by analyzing the rate of change of the correlation integral with the embedding dimension m. For a deterministic chaotic system, the correlation integral has the following relationship when the embedding dimension increases from m to m+1: In the formula, h2 is the correlation entropy of the system. From this, the calculation formula of the dynamic correlation entropy can be derived: The entropy calculation module 120 calculates the correlation integrals C m (r) and C m+1 (r) for embedding dimensions m and m+1, respectively, for the same set of data, and then substitutes them into the above formula to obtain the dynamic correlation entropy K2. The K2 entropy value is a scalar that quantifies the rate of orbit divergence in the phase space, i.e., the complexity of the dynamic behavior of the system. After the calculation is completed, the dynamic correlation entropy value is output to the local diagnosis module 130 for subsequent fault diagnosis.

[0054] After the dynamic correlation entropy is calculated in step S200, the process proceeds to step S300, in which the local diagnosis module 130 in each intelligent air switch terminal 100 performs real-time fault diagnosis on the dynamic correlation entropy sequence output by the entropy calculation module 120. The purpose of this step is to make a quick and accurate preliminary judgment of the electrical state of the current circuit, providing decision basis for subsequent protection actions or cooperative tracing. The specific diagnosis logic is as follows.

[0055] The local diagnostic module 130 receives a continuous time series of dynamic correlation entropy (K2) and executes at least one of the following diagnostic logics in parallel: The first diagnostic logic is for short-circuit or parallel arc fault. The physical feature of such fault is a sharp mutation of line current in a very short time, with a rapid rise in amplitude. This mutation is reflected in the current time series, which will instantly increase the complexity and uncertainty of its dynamic behavior, resulting in a large and pulsed jump in the dynamic correlation entropy K2 value output by the entropy calculation module 120. In order to capture this feature, the local diagnostic module 130 performs a first-order difference calculation on the received K2 series, i.e. ΔK2(t) = K2(t) - K2(t-1). When the absolute value of the first-order difference |ΔK2(t)| exceeds a first preset threshold value preset in the storage unit 105, it is determined that a short-circuit or parallel arc fault has occurred. The setting of the first preset threshold value is based on experimental data analysis of the rate of change of entropy under normal load switching (such as motor starting) and real fault conditions to ensure the reliability of the diagnosis.

[0056] The second diagnostic logic is for series arc fault. The physical feature of series arc fault is to produce an unstable and repeated arc and extinction plasma channel in the circuit. This process has high randomness and nonlinearity, which introduces a large amount of chaotic noise with wide frequency band into the current waveform, but the total current amplitude may not exceed the rated value. This continuous chaotic state keeps the complexity of the current time series at an abnormally high level. Therefore, the amplitude of its dynamic correlation entropy K2 will deviate from the low level of the normal load and fluctuate in a higher numerical interval. The local diagnostic module 130 judges the amplitude of K2, and when the amplitude is always higher than a preset second preset threshold value for a certain period of time (e.g. 50 milliseconds), it is determined that a series arc fault has occurred. The setting of the duration judgment is to distinguish it from non-persistent transient disturbances.

[0057] The third diagnostic logic is for gradual faults such as loose electrical connection or aging contact. The physical feature of such faults is that the contact resistance of the fault point increases slowly over time. On the one hand, the increased contact resistance will cause the line current to increase according to Joule's law (P = I 2R) generates additional heat at the contact, causing abnormal and persistent temperature rise of the contact compared to the surrounding environment (e.g. busbar). On the other hand, the non-linear change of contact resistance also causes weak and gradually increasing distortion of the current waveform, slowly increasing the dynamic complexity of the system. In order to diagnose such faults, the local diagnosis module 130 combines entropy analysis and temperature analysis. The module first receives the contact temperature sequence and busbar temperature sequence provided by the acquisition module 110, and obtains the differential temperature sequence by calculating the difference between the two. Then, the module simultaneously performs trend analysis on the dynamic correlation entropy sequence and the differential temperature sequence. When both the dynamic correlation entropy and the differential temperature show a persistent and monotonous increasing trend, it is determined that a gradual fault has occurred. The application of this double criterion effectively eliminates the temperature rise interference caused by normal changes in load current, significantly improving the accuracy of gradual fault diagnosis.

[0058] After performing local fault diagnosis at step S300, or when a serious fault that may affect the upper loop is diagnosed, the process proceeds to step S400 to perform collaborative tracing based on entropy migration spectrum. The core purpose of this step is to use the group perception capability of multiple intelligent air switch terminals 100 in the power distribution network to accurately locate and identify the source location and root type of the fault, rather than being limited to a single node. The specific implementation process is as follows.

[0059] Construction of entropy migration spectrum This stage first defines the generation and broadcast of entropy migration events. When the local diagnosis module 130 of any intelligent air switch terminal 100 detects a preset change in its dynamic correlation entropy (for example, in the diagnosis logic of S300, the first-order difference of the entropy value exceeds a first preset threshold or the amplitude of the entropy value continuously exceeds a second preset threshold), the terminal generates an entropy migration event. The event is encapsulated into a standard data packet and broadcast to other intelligent air switch terminals in the power distribution network through the communication module 140 of the intelligent air switch terminal. The content of the data packet at least includes: high-precision timestamp of the event occurrence, unique device ID of the intelligent air switch terminal generating the event, and amplitude of the dynamic correlation entropy measured at that moment.

[0060] Subsequently, the collaborative tracing module 150 of the intelligent air switch terminal 100 deployed at the upper level power distribution node (e.g. distribution box or main distribution box) is responsible for receiving and collecting a series of entropy migration events triggered by the same physical disturbance. The module sets a time window (e.g. 50 milliseconds) and groups all entropy migration events received within this time window and from different terminals downstream, thereby constructing the entropy migration spectrum. The entropy migration spectrum is a data set that records the entropy response of a single fault disturbance at different nodes of the power distribution network, and its specific form is a structure body containing a list of {device ID, timestamp, entropy value amplitude}.

[0061] After the entropy migration spectrum is constructed, the cooperative tracing module 150 of the superior intelligent air-break terminal performs calculation and analysis based on the spectrum to cooperatively trace the fault.

[0062] Firstly, the module calculates the time delay spectrum and the attenuation factor spectrum based on the time stamp and the entropy value amplitude of each entropy migration event in the entropy migration spectrum. The time delay spectrum is calculated as follows: in the entropy migration spectrum, the event node with the earliest time stamp is identified as the initial fault source node, and the delay amount of the time stamp of all other events in the spectrum relative to the time stamp of the source node is calculated. The time delay spectrum reveals the propagation path and sequence of the fault disturbance along the electrical line.

[0063] At the same time, the module calculates the attenuation factor spectrum. The cooperative tracing module 150 determines the fault source node (whose entropy value amplitude is denoted as K 2,s ) according to the time delay spectrum, and the entropy value amplitudes (denoted as K 2,s-1 to K 2,1 ) detected by the intelligent air-break terminals at each level in the power transmission direction from the source, and calculates the attenuation factor spectrum, the formula of which is as follows: In the formula, A ems is the attenuation factor spectrum, which is a vector composed of multiple ratios, representing the attenuation characteristics of the fault disturbance energy during the upward propagation.

[0064] Finally, the cooperative tracing module 150 determines the fault type according to the characteristics of the attenuation factor spectrum. The physical basis of this determination lies in the significant difference in energy propagation mode between different types of faults. When the attenuation factor spectrum presents a rapid attenuation characteristic (i.e., each ratio in the spectrum is much smaller than 1), it is determined to be a series fault. This is because the energy of a series arc fault and other series faults is mainly consumed at the fault point itself, and the disturbance propagating upward is greatly weakened. When the attenuation factor spectrum presents a slight attenuation characteristic (i.e., each ratio in the spectrum is close to 1), it is determined to be a parallel fault. This is because a short circuit and other parallel faults provide a new low-impedance discharge channel for the system, and the fault current propagates upward along the transmission path with minimal attenuation.

[0065] Finally, the cooperative tracing module 150 combines the fault propagation path determined by the time delay spectrum with the fault type determined by the attenuation factor spectrum to form a combined feature. The module matches and determines the fault type from the fault feature spectrum library pre-stored in the storage unit 105 according to the combined feature of the time delay spectrum and the attenuation factor spectrum. The final tracing result obtained through this matching process is transmitted to the protection execution module 160 for the implementation of more targeted cooperative protection actions.

[0066] After the local fault diagnosis and collaborative tracing are completed in steps S300 and S400 respectively, the flow enters step S500 to execute hierarchical collaborative protection actions. The purpose of this step is to execute the most appropriate circuit shutdown strategy according to the accurate diagnosis and tracing results obtained in the previous steps, so as to achieve rapid response, accurate isolation and systematic protection of the fault. This step is completed by the protection execution module 160 in each intelligent air switch terminal 100 by controlling the protection execution unit 103.

[0067] The protection execution unit 103 in this embodiment includes a power electronic switch (such as an IGBT or SiC-MOSFET module) and a traditional electromagnetic tripping mechanism. The former is used to perform fast shutdown in microseconds, and the latter is used to achieve permanent physical isolation in milliseconds. The protection execution module 160 executes hierarchical protection actions according to the results of local fault diagnosis and collaborative tracing, which specifically includes the following cases: The first case is to perform fast shutdown for local serious faults. When the result of local fault diagnosis is a short circuit or parallel arc fault, the diagnosis result is directly transmitted by the local diagnosis module 130 to the protection execution module 160. The intelligent air switch terminal directly drives the power electronic switch built-in the intelligent air switch terminal to perform fast shutdown of the circuit. Specifically, the protection execution module 160 immediately sends a shutdown control signal to the gate drive circuit of the power electronic switch. Since the response time of the power electronic switch is in the order of microseconds, this action can complete the shutdown of the circuit before the short circuit current rises to its first peak (usually within 10 ms of the power frequency cycle), effectively suppressing the release and impact of fault energy.

[0068] The second case is to perform backup protection for the result of collaborative tracing. When the result of collaborative tracing is a parallel fault, this result is obtained by the collaborative tracing module 150 of the upper-level intelligent air switch terminal that has collected the entropy migration spectrum. At this time, the upper-level intelligent air switch terminal that has collected the entropy migration spectrum issues a pre-blocking instruction to the upper-level intelligent air switch terminal to which it belongs. The instruction is sent through the communication module 140. The protection execution module 160 of the upper-level intelligent air switch terminal receiving the instruction will analyze the instruction to make the power electronic switch of the upper-level intelligent air switch terminal enter a standby state for collaborative backup protection. This standby state means that the shutdown threshold of the power electronic switch is temporarily lowered or its drive circuit is pre-charged, and once the current of the current level exceeds a certain threshold or a trip confirmation instruction of the next level is received, the power electronic switch can act instantly, thereby constituting a fast and reliable backup support for the protection of the next level.

[0069] The third case is to perform the final physical disconnection to ensure safety. After the fast disconnection of the power electronic switch, whether based on local diagnosis or collaborative instruction, it has only completed the electrical shutdown of the circuit, and the physical contacts have not been separated. To achieve permanent electrical isolation, the intelligent air switch terminal delays the driving of the electromagnetic tripping mechanism built-in the intelligent air switch terminal to complete the final physical disconnection. Specifically, after the power electronic switch is turned off, the protection execution module 160 starts a preset delay (for example, 20 milliseconds). After the delay ends, the module drives a coil to generate sufficient electromagnetic force to actuate the mechanical tripping mechanism to physically separate the moving and static contacts, forming a visible break point. This two-stage disconnection method of "electronic first and mechanical second" combines the speed of electronic switches and the isolation reliability of mechanical switches.

[0070] Referring to the drawings Figure 1 The multi-level collaborative fault detection method of the embodiment of the present application further includes a step S600 of archiving and analyzing fault data. This step is optional, but plays an important role in improving the operation and maintenance level of the entire power distribution system and the continuous optimization of the fault diagnosis model.

[0071] There are two execution occasions for this step: after performing the hierarchical protection action, i.e., when a complete fault event has been disposed of; or when a gradual fault is determined based on local fault diagnosis, i.e., when the system issues a warning but has not yet performed a trip action.

[0072] After any of the above cases occurs, the data management module 170 inside the intelligent air switch terminal 100 is activated. This module is responsible for collecting all data related to this event and organizing it into a structured fault data record. The fault data, including the local fault diagnosis result, the collaborative traceability result, the entropy migration spectrum, and the multi-physical quantity time sequence signal, are uploaded to the cloud platform for archiving.

[0073] Specifically, the data packet content collected by the data management module 170 includes: Event metadata: including event unique ID, intelligent air switch terminal ID where the event occurred, high-precision timestamp of the event occurrence, and final disposal action (such as trip, warning).

[0074] Diagnosis and traceability results: local fault diagnosis result (for example, determining a series arc) output by the local diagnosis module 130, and collaborative traceability result (for example, determining that the fault source is a certain device downstream, and the type is a parallel fault) output by the collaborative traceability module 150.

[0075] Entropy migration spectrum data: if the event triggers collaborative traceability, it contains the complete entropy migration spectrum collected by the upper node, i.e., a data structure containing a list of {device ID, timestamp, entropy value amplitude}.

[0076] Raw time series signals: all the multi-physical quantity time series signals collected by the acquisition module 110 within a period of time before and after the fault occurs (for example, 200 milliseconds before the fault point to 100 milliseconds after the fault point), that is, high-resolution current time series, voltage time series, contact temperature series and bus temperature series.

[0077] The data management module 170 packages and compresses all the above data, and then uploads the data package to the preset cloud platform 200 through the communication module 140 via the Internet. After receiving the data, the cloud platform 200 parses and persistently stores it, forming a fault database that can be queried and analyzed for a long time. This database not only provides complete on-site data for post-fault manual analysis, but also provides a data basis for using machine learning algorithms to deeply mine massive historical fault data, continuously optimize threshold parameters in the local diagnosis module 130 and the collaborative traceability module 150, and provide a fault feature spectrum library.

[0078] Although embodiments of the present application have been shown and described, it is to be understood that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-stage collaborative fault detection method based on intelligent air switching, characterized in that, The method comprises the following steps: Synchronously collecting multi-physical quantity time series signals by intelligent air break switch terminals deployed at each level of a power distribution network; Reconstructing a phase space of the collected current time series by the intelligent air break switch terminals, and calculating a dynamic correlation entropy reflecting dynamic behavior of a power loop; Performing local fault diagnosis by the intelligent air break switch terminals based on the dynamic correlation entropy; Performing collaborative communication among the intelligent air break switch terminals, constructing an entropy migration spectrum based on the dynamic correlation entropy of each air break switch terminal, and collaboratively tracing a fault according to the entropy migration spectrum; Performing hierarchical protection actions according to results of the local fault diagnosis and the collaborative fault tracing.

2. The multi-stage collaborative fault detection method based on intelligent air switching according to claim 1, characterized in that, The step of synchronously collecting multi-physical quantity time series signals by intelligent air break switch terminals deployed at each level of a power distribution network comprises the following steps: After completing time synchronization by a network time protocol or a GPS module, each intelligent air break switch terminal deployed at each level of the power distribution network collects the multi-physical quantity time series signals, wherein the multi-physical quantity time series signals comprise current time series, voltage time series, contact temperature series, and bus temperature series.

3. The multi-level collaborative fault detection method based on intelligent air switching according to claim 1, characterized in that, The step of reconstructing a phase space of the collected current time series by the intelligent air break switch terminals, and calculating a dynamic correlation entropy reflecting dynamic behavior of a power loop comprises the following steps: reconstructing the current time series I(i) into a state vector Y in a m-dimensional phase space by a time-delay embedding method i to build an attractor capable of characterizing the dynamic behavior of the power circuit under study: Y i = [I(i), I(i+τ),..., I(i+(m-1)τ)]; In the formula, I(i) represents a current sampling value at discrete time index i; m represents an embedding dimension; and τ represents a time delay. based on the state vector Y i The correlation integral is calculated, and the dynamic correlation entropy is determined as a quantitative index of the dynamic behavior complexity of the power loop under study according to the rate of change of the correlation integral with the embedding dimension m.

4. The multi-stage collaborative fault detection method based on intelligent air switching according to claim 1, characterized in that, The step of performing local fault diagnosis by the intelligent air break switch terminals based on the dynamic correlation entropy comprises the following steps: At least one of the following diagnostic logics is performed: Calculating a first-order difference of the dynamic correlation entropy, and determining a short circuit or parallel arc fault when the first-order difference exceeds a first preset threshold value; Determining an amplitude of the dynamic correlation entropy, determining a series arc fault when the amplitude continuously exceeds a second preset threshold value, and determining a gradual fault when both the dynamic correlation entropy and a differential temperature obtained by calculating a difference between the contact temperature series and the bus temperature series continuously increase.

5. The multi-level collaborative fault detection method based on intelligent air switching according to claim 1, characterized in that, The step of performing collaborative communication among the intelligent air break switch terminals, constructing an entropy migration spectrum based on the dynamic correlation entropy of each air break switch terminal, and collaboratively tracing a fault according to the entropy migration spectrum comprises the following steps: When any intelligent air break switch terminal detects a preset change in the dynamic correlation entropy, the intelligent air break switch terminal generates an entropy migration event, and broadcasts event information including a time stamp of the event and an amplitude of the entropy to other intelligent air break switch terminals in the power distribution network through a communication module of the intelligent air break switch terminal; 6. The multi-stage collaborative fault detection method based on intelligent air switching according to claim 5, characterized in that, A superior intelligent air break switch terminal receives and collects a series of entropy migration events caused by a same physical disturbance to form the entropy migration spectrum. The step of collaboratively tracing a fault according to the entropy migration spectrum comprises the following steps: Based on the time stamp and the amplitude of the entropy of each entropy migration event in the entropy migration spectrum, a time delay spectrum and an attenuation factor spectrum are calculated. According to the combination of the time delay spectrum and the attenuation factor spectrum, a fault type is matched and determined from a preset fault feature spectrum library.

7. The multi-stage collaborative fault detection method based on intelligent air switching according to claim 6, characterized in that, The attenuation factor spectrum is calculated in the following manner: In the formula, A ems is an attenuation factor spectrum; K 2,s is an entropy value amplitude of a fault source node; K 2,s-1 to K 2,1 are respectively the entropy value amplitudes detected by the intelligent air switch terminals at each level in the power transmission direction from the fault source. According to the feature of the attenuation factor spectrum, a fault type is determined: When the attenuation factor spectrum presents a rapid attenuation feature, it is determined as a series type fault; When the attenuation factor spectrum presents a slight attenuation feature, it is determined as a parallel type fault.

8. The multi-level collaborative fault detection method based on intelligent air switching according to claim 1, characterized in that, The step of performing a hierarchical protection action according to the results of the local fault diagnosis and the collaborative tracing includes: When the result of the local fault diagnosis is a short circuit or a parallel arc fault, the power electronic switch built in the intelligent air switch terminal directly driven by the intelligent air switch terminal making the diagnosis is used to perform a rapid cut-off of the circuit; When the result of the collaborative tracing is a parallel type fault, a pre-blocking instruction is issued by the upper-level intelligent air switch terminal collecting the entropy migration spectrum to the intelligent air switch terminal of a higher level to which the upper-level intelligent air switch terminal belongs, so that the power electronic switch of the intelligent air switch terminal of the higher level enters a standby state to perform collaborative backup protection; After the power electronic switch performs the rapid cut-off, the electromagnetic tripping mechanism built in the intelligent air switch terminal is driven by the intelligent air switch terminal with a time delay to complete the final physical disconnection.

9. The multi-level collaborative fault detection method based on intelligent air switching according to claim 1, characterized in that, The method further includes: After the hierarchical protection action is performed, or when it is determined as a gradual change fault according to the local fault diagnosis, fault data including the result of the local fault diagnosis, the result of the collaborative tracing, the entropy migration spectrum, and the multi-physical quantity time sequence signal are uploaded to a cloud platform for archiving.

10. A multi-level cooperative fault detection system based on intelligent air switching, applied to a multi-level cooperative fault detection method based on intelligent air switching according to any one of claims 1-9, characterized in that, The method includes a plurality of intelligent air switch terminals deployed at each level of a power distribution network, and each functional module of the intelligent air switch terminal includes: a collection module for synchronously collecting multi-physical quantity time sequence signals; an entropy calculation module for phase space reconstruction of the current time sequence collected by the collection module and calculation of a dynamic correlation entropy reflecting the dynamic behavior of the power circuit; a local diagnosis module for performing local fault diagnosis based on the dynamic correlation entropy; a communication module for enabling the intelligent air switch terminal to collaboratively communicate with other intelligent air switch terminals; a collaborative tracing module for constructing an entropy migration spectrum based on the dynamic correlation entropy of each terminal through the collaborative communication, and collaboratively tracing a fault according to the entropy migration spectrum; a protection execution module for performing a hierarchical protection action according to the diagnosis result of the local diagnosis module and the tracing result of the collaborative tracing module.

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