Method and device for preventing mis-trip of distance protection with large distance interval ring

Abstract

The application provides a method and device for preventing mis-trip of distance protection in a large distance interval ring, fault propagation records of interval ring circuit breaker nodes are collected to construct a mis-trip risk conduction matrix, and a fault path is tracked; wavelet decomposition is used to perform frequency spectrum analysis on the tracked path, and a key protection section is identified; impedance aggregation states and power transmission trajectories of each node are extracted, an energy distribution mode is established, and an impedance-power-energy coupling situation is formed; a time window is activated based on the coupling situation, a cooperative distance protection potential field is constructed through a communication protocol, a gradient scan is performed to construct a cooperative network, a multi-dimensional protection decision domain is generated through vector superposition, an optimal balance point is located through global search, a hierarchical protection setting sequence is generated, a cooperative adjustment strategy is formulated, a transition from passive to active is realized for the large distance interval ring protection, and intelligent mis-trip prevention is completed through real-time monitoring of dynamic impedance state identification of a transition critical point and triggering of the cooperative adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system relay protection, in particular to a method and device for preventing mis-trip of distance protection in a large-distance interval ring. BACKGROUND

[0002] With the continuous expansion of the scale of the power system and the increasing complexity of the interval ring structure, the problem of mis-trip of distance protection in a large-distance interval ring transmission system is increasingly prominent. In the interval ring operation mode, factors such as multi-path shunting of fault current, mutual influence of zero sequence mutual inductance, and system oscillation cause the measured impedance of the traditional distance protection device to deviate, the protection range to be improperly expanded or reduced, and the adjacent line to be easily triggered by a chain of mis-trip, which seriously threatens the safe and stable operation of the power grid.

[0003] The existing distance protection technology mainly judges faults based on local information, lacks the ability to perceive the global operation situation of the interval ring, and is difficult to accurately identify and prevent mis-trip risks. At the same time, each protection device acts independently, lacks an effective coordination mechanism, and cannot achieve optimal coordination in complex fault scenarios. Therefore, it is urgent to develop a new type of mis-trip prevention protection technology that can comprehensively utilize multi-dimensional information and achieve intelligent coordination. SUMMARY

[0004] The present application provides a method and device for preventing mis-trip of distance protection in a large-distance interval ring, which aims to quantify fault propagation risks by constructing a mis-trip risk conduction matrix, extract mutation spectrum features by using wavelet decomposition technology to locate key sections, establish an impedance-power-energy coupling situation to reveal the multi-dimensional association of the system, construct a collaborative distance protection potential field to achieve multi-node linkage protection, obtain a balance between reliability and selectivity through global optimization of the multi-dimensional protection decision domain, realize active defense based on dynamic impedance state monitoring and transition critical point identification, and form an intelligent mis-trip prevention protection system with prediction, coordination, optimization, and regulation capabilities.

[0005] The present application provides a method and device for preventing mis-trip of distance protection in a large-distance interval ring, which aims to quantify fault propagation risks by constructing a mis-trip risk conduction matrix, extract mutation spectrum features by using wavelet decomposition technology to locate key sections, establish an impedance-power-energy coupling situation to reveal the multi-dimensional association of the system, construct a collaborative distance protection potential field to achieve multi-node linkage protection, obtain a balance between reliability and selectivity through global optimization of the multi-dimensional protection decision domain, realize active defense based on dynamic impedance state monitoring and transition critical point identification, and form an intelligent mis-trip prevention protection system with prediction, coordination, optimization, and regulation capabilities.

[0006] Collecting fault propagation records of each circuit breaker node in the large-distance interval ring, and structuring the fault propagation records to construct a mis-trip risk conduction matrix;

[0007] Using the mis-trip risk conduction matrix to track fault impact paths, performing wavelet decomposition on the fault impact paths to obtain mutation spectrum features, and determining key sections that need to be pre-set protection margins according to the mutation spectrum features;

[0008] extracting impedance aggregation states and power transmission trajectories of interval ring nodes in the key section, locating an impedance jump region from the impedance aggregation states, constructing an energy distribution mode based on the impedance jump region, and phase-coupling the energy distribution mode with the power transmission trajectories to form an impedance-power-energy coupling state;

[0009] activating a protection adjustment time window based on the impedance-power-energy coupling state, establishing an adjacent node communication protocol based on the protection adjustment time window, collecting adjacent state information through the adjacent node communication protocol, and constructing a cooperative distance protection potential field based on the adjacent state information;

[0010] performing protection range gradient scanning in the cooperative distance protection potential field, constructing an inter-node cooperative network based on the scanning results, performing energy distribution analysis on the cooperative network to determine a cooperative coverage scope, and performing multi-dimensional synthesis on the scope, the key section and the impedance-power-energy coupling state by vector superposition to generate a multi-dimensional protection decision domain;

[0011] implementing global search on the multi-dimensional protection decision domain, locating a protection reliability and selectivity optimal balance point from the search results, generating a hierarchical protection setting sequence based on the balance point, formulating a node cooperative adjustment strategy based on the cooperative network, and optimizing the hierarchical protection setting sequence through the adjustment strategy to output an optimal protection configuration;

[0012] monitoring the dynamic impedance state of the interval ring in real time after the optimal protection configuration is executed, identifying a protection state transition critical point based on the dynamic impedance state, triggering the node cooperative adjustment strategy based on the protection state transition critical point, and completing intelligent anti-malfunction trip protection.

[0013] The second aspect of the application provides a large-distance interval ring distance protection anti-malfunction trip device, comprising:

[0014] a fault processing module configured to collect fault propagation records of circuit breaker nodes in the large-distance interval ring, and construct a malfunction trip risk conduction matrix by structuring the fault propagation records;

[0015] a spectrum analysis module configured to perform fault impact path tracking by using the malfunction trip risk conduction matrix, perform wavelet decomposition on the fault impact path to obtain mutation spectrum features, and determine a key section requiring preset protection margin according to the mutation spectrum features;

[0016] an energy state module configured to extract impedance aggregation states and power transmission trajectories of interval ring nodes in the key section, locate an impedance jump region from the impedance aggregation states, construct an energy distribution mode based on the impedance jump region, and phase-couple the energy distribution mode with the power transmission trajectories to form an impedance-power-energy coupling state.

[0017] a potential field construction module configured to activate a protection adjustment time window based on the impedance-power-energy coupling state, establish an adjacent node communication protocol based on the protection adjustment time window, collect adjacent state information through the adjacent node communication protocol, and construct a collaborative distance protection potential field based on the adjacent state information;

[0018] a decision generation module configured to perform a protection range gradient scan in the collaborative distance protection potential field, construct an inter-node collaboration network based on the scan results, perform energy distribution analysis on the collaboration network to determine a collaborative coverage scope, and perform multi-dimensional synthesis of the scope, the key section, and the impedance-power-energy coupling state by vector superposition to generate a multi-dimensional protection decision domain;

[0019] a configuration optimization module configured to perform global search on the multi-dimensional protection decision domain, locate an optimal balance point of protection reliability and selectivity from the search results, generate a hierarchical protection setting sequence based on the balance point, develop a node collaborative adjustment strategy based on the collaboration network, and output an optimal protection configuration by optimizing the hierarchical protection setting sequence through the adjustment strategy;

[0020] a dynamic adjustment module configured to monitor a dynamic impedance state of the interval ring in real time after the optimal protection configuration is executed, identify a protection state transition critical point based on the dynamic impedance state, trigger the node collaborative adjustment strategy based on the protection state transition critical point, and complete intelligent anti-malfunction tripping protection.

[0021] The beneficial effects of the present application are embodied in the following aspects: first, by constructing a malfunction tripping risk conduction matrix and performing fault impact path tracking, combined with the sudden spectrum features extracted by wavelet decomposition, the quantitative analysis of the interval ring fault propagation mechanism and the accurate identification of the key section are realized, compared with the traditional protection mode based on local information, the potential malfunction tripping risk can be predicted from a global perspective, providing a quantitative basis for protection configuration optimization. Second, the impedance-power-energy coupling state and the collaborative distance protection potential field are established, through the construction of the inter-node collaboration network and the multi-dimensional protection decision domain, the limitations of traditional protection devices acting independently are broken through, the intelligent collaboration and global optimization of multiple nodes are realized, and the chain tripping accidents caused by protection mismatch are effectively avoided. Finally, based on the real-time monitoring of the dynamic impedance state and the identification mechanism of the protection state transition critical point, combined with the dynamic triggering of the node collaborative adjustment strategy, the technical transformation from passive response to proactive defense is realized, and the power supply reliability of the large-distance interval ring under complex operating conditions is improved.

[0022] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0023] The drawings hereof show specific examples of the technical solutions of the present application, and form part of the description, for explaining the technical solutions, principles and effects of the present application.

[0024] Unless specifically described or defined otherwise, the same reference signs in different drawings represent the same or similar technical features, and different reference signs may also be used to represent the same or similar technical features.

[0025] Figure 1 is a flowchart of a method for preventing mis-trip of distance protection of a large-distance interval ring.

[0026] Figure 2 is a structural block diagram of a device for preventing mis-trip of distance protection of a large-distance interval ring. DETAILED DESCRIPTION

[0027] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0028] In this specification, the reference to “one embodiment” or “some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrases “in one embodiment”, “in some embodiments”, “in other embodiments”, “in additional embodiments”, and so on, in various places in the specification are not necessarily all referring to the same embodiment, unless otherwise specifically stated. The terms “including”, “containing”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise.

[0029] The technical solutions of the embodiments of the present application are described below.

[0030] As shown in Figure 1 The embodiments of the present application provide a method for preventing mis-trip of distance protection of a large-distance interval ring, which comprises the following steps S110-S170:

[0031] In step S110, the fault propagation records of each circuit breaker node in the large-distance interval ring are collected, and the fault propagation records are structured and processed to construct a mis-trip risk conduction matrix.

[0032] Specifically, the distributed fault monitoring device is deployed in the large distance interval ring system to collect the fault propagation data of each circuit breaker node in real time. The interval ring topology includes multiple substation nodes interconnected by high-voltage transmission lines to form a closed-loop structure, and each node is configured with a circuit breaker, a mutual inductor, and a protection device. The fault propagation record collection adopts a combination of a synchronous phasor measurement unit (PMU) and a fault recorder. The PMU records voltage and current phasor data at a sampling rate of 100 frames per second, and the fault recorder records transient waveforms at a sampling rate of 10 kHz when a disturbance is detected. The key parameters collected include three-phase voltage amplitude and phase angle, three-phase current amplitude and phase angle, zero-sequence current, negative-sequence current, circuit breaker state (0-trip / 1-close), protection action signal, and timestamp. Time synchronization uses GPS time service with an accuracy of 1 μs to ensure the time consistency of data from different nodes. The fault identification algorithm triggers recording by detecting that the current mutation exceeds the threshold, and the calculation formula is ΔI = |I(t)-I(t-T)|, where ΔI is the current mutation, I(t) is the current instantaneous value at time t, I(t-T) is the current value at the same time in the previous cycle, and T is the power frequency period of 20 ms. After the fault recorder is triggered, the recording time is automatically extended to 2 seconds after the fault is eliminated to ensure that the complete fault evolution process is captured. Data transmission uses the IEC61850 protocol and is collected through an optical fiber communication network to the dispatch center to form a complete fault propagation record database.

[0033] In some embodiments, the structured processing of the fault propagation record constructs a mis-trip risk conduction matrix, including: performing pattern mining on the fault propagation record to extract historical fault patterns; identifying impedance mutation sensitive nodes based on the historical fault patterns; determining distance protection boundary offset from the impedance mutation sensitive nodes; and constructing a mis-trip risk conduction matrix according to the distance protection boundary offset.

[0034] The historical fault patterns are extracted by pattern mining on the fault propagation records. The voltage and current phasor data and circuit breaker action sequence are processed to identify the fault events by sliding window detection algorithm. The fault sequence is defined as the combination of circuit breaker action timing and corresponding current amplitude variation, and the improved Apriori algorithm is used to mine the frequent sequences. The fault propagation time delay is extracted from the phase angle data recorded by PMU, and the time delay relationship between different nodes is determined by cross-correlation analysis. The fault propagation path is determined by analyzing the sequence of circuit breaker state changes, and the 0→1 state transition is marked as a protection action. The pattern classification is divided into three categories: radial propagation, loop propagation and oscillation propagation, according to the characteristics of the propagation path. The radial propagation shows that the fault impact attenuates along a single path, the loop propagation forms a feedback effect through the loop, and the oscillation propagation produces multiple reflections and superpositions between nodes. The feature vector of the fault pattern contains multiple parameters such as propagation speed, attenuation coefficient and impact range, and the clustering algorithm is used to classify and store similar patterns. The time correlation analysis finds that most of the mis-trip events occur in the transient process after the fault, and this time window becomes an important pattern feature. The spatial correlation is determined by calculating the fault correlation coefficient between different node pairs, and the nodes with close electrical distance show strong correlation.

[0035] The impedance mutation sensitive nodes are identified based on historical fault patterns. The impedance calculation uses the voltage and current phasor data recorded in the historical fault patterns, and the positive sequence impedance calculation formula is Z1=V1 / I1, where Z1 is the positive sequence impedance, V1 is the positive sequence voltage phasor, and I1 is the positive sequence current phasor. The sensitivity analysis focuses on the nodes with rapid impedance changes in the fault pattern, and the sensitivity degree is identified by calculating the impedance change rate. The nodes with frequent mis-trip are selected from the historical fault propagation paths, and the common feature of these nodes is that the measured impedance changes abruptly at the moment of fault. The impedance trajectory analysis uses the continuous impedance values during the fault, and the trajectory is drawn in the R-X plane to identify the cases where the protection boundary is quickly crossed. The calculation of the measured impedance uses the full-cycle Fourier algorithm, which is updated once every cycle, and may produce transient errors in the early stage of the fault. The interval ring topology configuration information is used to identify the double-circuit line sections on the same tower, and for these double-circuit lines, the influence of zero-sequence mutual inductance needs to be considered, and the corrected measured impedance is Z'm=Z1-k0×Z0m, where k0 is the zero-sequence compensation coefficient calculated by the ratio of zero-sequence impedance to positive-sequence impedance, and Z0m is the mutual inductance impedance. The interval ring has the speciality that the impedance sensitivity difference caused by uneven power distribution, and the nodes show different sensitive characteristics under different operating modes. The node classification is divided into three categories of sensitive nodes: power source side, load side and tie point, according to their position in the fault propagation path and impedance change characteristics.

[0036] The distance protection boundary offset is determined from the impedance mutation sensitive node. The offset calculation formula is ΔZ = Z_measured - Z_set, where ΔZ is the impedance offset, Z_measured is the actual measured impedance of the node, and Z_set is the setting impedance of the protection device. The influence of the load current is determined by analyzing the load level of the sensitive node, and the offset phenomenon is more obvious under heavy load conditions. The influence of system oscillation on the sensitive node is quantified by the power angle difference data, providing an important reference for offset analysis. Historical data statistics of the sensitive node show that most of the mis-trips are caused by the expansion of the protection range, i.e., the measured impedance is offset to the inside of the circle. The calculation of the offset needs to consider the influence of CT saturation. When saturated, the measured current is smaller, resulting in a larger calculated impedance, which may cause the protection to refuse to act. The influence of the transition resistance on the offset is manifested as a decrease in the impedance angle, causing the measured impedance to deviate from the impedance angle of the fault line, which is particularly evident in high-resistance ground faults. The offset direction is closely related to the type of sensitive node: the power side node mainly shows a deviation in the reactance component, and the load side node shows a deviation in the resistance component. The dynamic offset tracks the real-time state changes of the sensitive node, providing offset reference values for different operating conditions.

[0037] A mis-trip risk conduction matrix is constructed according to the distance protection boundary offset. The element calculation formula of the risk conduction matrix R is R[i, j] = P[i, j] x (|ΔZ[j]| / Z_base), where R[i, j] is the risk value of node i fault causing node j mis-trip, P[i, j] is the fault propagation probability, ΔZ[j] is the protection boundary offset of node j, and Z_base is the reference impedance. The propagation probability is calculated by the ratio of the frequency of node j mis-trip when node i fails to the total number of node i faults, establishing a quantitative relationship between the influence of node faults. The nodes with larger offset values correspond to higher risk values in the matrix, forming a quantitative representation of risk distribution. The sparsity of the matrix reflects the electrical connection relationship, and only nodes with close electrical distance have significant risk conduction. Block processing technology is used in matrix construction to divide large-scale interval rings according to voltage levels and regions, and each sub-block is calculated independently before merging to improve calculation efficiency. The risk value calculation introduces a directional weight factor to consider the directional characteristics of protection configuration, and the risk weight of positive direction faults is greater than that of negative direction. The asymmetry of the risk value reflects the influence of power flow direction on fault propagation. The offset data is organized according to node number and connection relationship, and finally a complete risk conduction matrix is formed.

[0038] In step S120, the mis-trip risk conduction matrix is used to track the fault impact path, wavelet decomposition is performed on the fault impact path to obtain the mutation spectrum feature, and the key section that needs to be pre-set protection margin is determined according to the mutation spectrum feature.

[0039] Specifically, the system traces the fault impact paths using the risk conduction matrix R. The path tracing algorithm starts from the element R[i, j] with the highest risk value in the matrix and uses a depth-first search strategy to find all possible propagation paths. A fault impact path is defined as a sequence of nodes P = {n1→n2→...→n k} where any adjacent node satisfies R[nᵢ, nᵢ +1 ] > R_th, and R_th is the risk threshold. The path weight calculation uses a cumulative risk model, W_path=Π(1-R[nᵢ, nᵢ +1 ]), representing the overall reliability of the path. Multi-path concurrent analysis identifies simultaneously activated propagation channels, and when multiple paths share part of the nodes, the integrated risk of the node is determined by the maximum value principle. The path search uses a pruning strategy to improve efficiency, and when the cumulative weight is less than 0.1, the search of the branch is terminated. The path storage uses an adjacency list structure, and each node records all its reachable downstream nodes and the corresponding risk values, facilitating fast traversal. Key path screening retains the main propagation channels with a weight greater than 0.7, which covers the main fault propagation scenarios in the system. Path topology feature extraction includes structural parameters such as path length, branch point number, loop characteristics, etc. The identification of fault impact paths determines the precise analysis range for subsequent spectral analysis.

[0040] The wavelet decomposition is performed on the tracked fault impact path, and the mutation spectrum features of each node on the path are extracted. According to the node sequence determined by the path tracking, the PMU record data of the corresponding node at the historical fault event occurrence time used when constructing the risk transmission matrix is called, the current waveform during the fault is extracted, and the sampling rate is consistent with that of the fault recorder in S110 (10 kHz). The wavelet decomposition uses Daubechies wavelet basis function, and the decomposition layer is set to 5 layers, covering the frequency range from power frequency to 5 kHz. The current waveform of each node on the path is continuously wavelet transformed, and the calculation formula is CWT(a, b) = (1 / √a)∫f(t)ψ*((t-b) / a)dt, where a is the scale parameter, b is the time shift parameter, ψ is the wavelet mother function, and f(t) is the fault current signal. The high-frequency detail coefficient reflects the transient characteristics at the fault moment, and the low-frequency approximation coefficient contains the steady-state component information. The mutation point detection is realized by calculating the modulus maximum value of the wavelet coefficient, and when the modulus maximum value exceeds the set threshold, it is marked as a mutation point. The wavelet coefficient calculation uses a fast algorithm, and the Mallat tower algorithm is used to reduce the calculation complexity from O(N²) to O(NlogN). The boundary effect processing uses the symmetric extension method, and the signal is mirror extended by half window length at both ends to avoid boundary distortion. The calculation of the energy distribution of the spectrum reveals the energy proportion change law of different frequency bands. The feature band identification finds that the energy surge in the 500Hz-2kHz frequency band is highly related to the mis-trip event. The mutation spectrum features of each node form a complete path spectrum feature map.

[0041] According to the mutation spectrum characteristics obtained by wavelet decomposition, the key section needing to preset the protection margin is determined. The nodes on the fault impact path are reordered according to their spatial positions in the interval ring, and then the spectral characteristics of the spatially adjacent nodes are aggregated, and the risk index of each section is the maximum value of the indexes of all nodes in the section. The key section identification comprehensively considers the spectral mutation strength and duration, and defines the risk index D_risk=A_spike×T_duration, where A_spike is the spectral mutation amplitude and T_duration is the duration. The mapping relationship between the spectral characteristics and the risk of misoperation is established through historical data statistics, and the section with the high-frequency energy ratio exceeding the threshold is marked as a high-risk section. The protection margin calculation is based on the statistical distribution of the spectral characteristics, and the margin value Z_margin=Z_set×(1+k_safe×σ_freq), where Z_set is the current setting impedance value of each node to the protection device, k_safe is the safety factor, and σ_freq is the standard deviation of the spectral characteristics. The section division adopts the sliding window method, and the window length is 5 nodes and the step length is 1 node, which ensures that no potential dangerous area is missed. The margin coordination of adjacent sections adopts a smooth transition strategy to avoid the coordination disorder caused by the mutation of the protection setting value. For example, at the power source access point of the interval ring, wavelet decomposition shows that there is a persistent energy aggregation in the 2-3 kHz frequency band, which corresponds to the generator subsynchronous oscillation frequency, so this section is marked as a key section that needs to enhance the protection margin. The margin classification is determined according to the risk index D_risk: strong mutation section, medium mutation section and slight mutation section correspond to different margin levels respectively. The spatial correlation analysis finds that the spectral characteristics of adjacent sections have a contagious effect and need to be considered coordinately. The key sections are mainly distributed at the access points of large power sources, load centers and the middle positions of long-distance transmission lines, and finally form the key sections that need to be protected.

[0042] In step S130, the impedance aggregation state and the power transmission trajectory of each interval ring node in the key section are extracted, the impedance jump region is located from the impedance aggregation state, the energy distribution mode is constructed based on the impedance jump region, and the impedance-power-energy coupling situation is formed by phase correlation between the energy distribution mode and the power transmission trajectory.

[0043] Specifically, based on the risk index D_risk of the key section, different risk levels of sections are extracted by using differentiated acquisition strategies for the impedance aggregation state and the power transmission trajectory of each interval ring node: the frequency of strong mutation sections is increased to 50 points per cycle, the frequency of medium mutation sections is 30 points, and the frequency of slight mutation sections remains 20 points. The impedance aggregation state is formed by collecting impedance measurement values of nodes under different operating conditions, the acquisition frequency is differentiated according to the risk level, and the impedance cloud chart Z_cloud={Z1, Z2, …, Z n}, wherein each Zᵢ contains resistance and reactance components. The impedance aggregate focuses on the impedance response characteristics in the 500Hz-2kHz frequency band within the critical section, which is confirmed to be highly related to the mis-trip in the aforementioned analysis. The power transfer trajectory records the curves of active power P and reactive power Q changing with time, with the sampling interval synchronized with the PMU data. The recording window of the power transfer trajectory is adjusted according to the mutation duration of the critical section: a high-frequency window is used for short transient section (<100ms), and a long-time window is used for long transient section (>1s). The impedance cloud map of the nodes within the critical section is constructed by considering the spectral energy distribution weight, giving higher sampling density to the impedance changes in high-energy frequency bands. The data preprocessing eliminates bad measurement data, identifies outliers using the 3σ criterion, and ensures the reliability of the impedance and power data, finally obtaining the impedance aggregate and power transfer trajectory of each interval ring node.

[0044] The impedance jump region is located from the extracted impedance aggregate, and the critical position of rapid impedance change is identified. The impedance jump detection uses a gradient algorithm to calculate the impedance change rate |ΔZ / Δt| of adjacent sampling points, and marks a jump point when the change rate exceeds the set threshold. The jump region is defined as the spatial aggregation area of the jump points, and the DBSCAN clustering algorithm is used to classify the jump points with a distance less than ε into the same region. The mutation of the impedance angle often reflects the change of the system state more than the change of the impedance amplitude, and the change of the impedance angle exceeding 15° is monitored. The jump intensity quantitative index J_intensity=max(|ΔZ|)×N_jump, where |ΔZ| is the maximum impedance change, and N_jump is the number of jumps per unit time. Spatial distribution analysis shows that the impedance jump region is mainly concentrated in the large-capacity generator connection point, the HVDC converter station near area, and the convergence point of multi-loop lines. The boundary of the jump region is determined by the contour method, and the points with the same impedance change rate are connected to form a closed region. The statistical distribution of the jump duration presents a bimodal characteristic, and the short-time jump (<100ms) is related to the switch operation, and the long-time jump (>1s) is related to the system oscillation. The impedance jump region is successfully located from the impedance aggregate through gradient detection and spatial clustering.

[0045] In some embodiments, the energy distribution pattern is constructed based on the impedance jump region, including: extracting energy dispersion features based on the impedance jump region; performing spatial mapping on the energy dispersion features to obtain an energy distribution contour; identifying energy concentration nodes based on the energy distribution contour; and constructing an energy distribution pattern using the energy concentration nodes.

[0046] The energy scattering characteristics are extracted based on the impedance jump region. The energy is calculated using the instantaneous power integration method, E = ∫P(t)dt, where E is the energy and P(t) is the instantaneous power in the jump region. The energy flow direction is determined by the positive and negative signs of the power, with positive values indicating energy inflow and negative values indicating energy outflow. The energy change rate dE / dt of the jump region reflects the dynamic characteristics of energy scattering, with rapid growth indicating energy accumulation and rapid decline indicating energy divergence. The energy density calculation considers the electrical capacity of the region, ρ_E = E / S_rated, where S_rated is the rated capacity of the region, ensuring the comparability of regions with different capacities. Time scale analysis of the scattering characteristics finds that the millisecond-level rapid scattering is related to the transient process, and the second-level scattering is related to the protection action. The energy oscillation mode is extracted through FFT analysis, identifying the dominant oscillation frequency and damping characteristics. The energy redistribution process triggered by the jump has directionality, preferentially transferring to low impedance paths. The spatial range of energy scattering is determined by the electrical distance, with an influence radius of about 3-5 electrical nodes. The energy scattering characteristics in the jump region show obvious spatio-temporal distribution rules and directionality characteristics.

[0047] The energy distribution profile is obtained by spatial mapping of the energy scattering characteristics. The energy density ρ_E and energy change rate dE / dt in the energy scattering characteristics are used as input data for the mapping, and the geographical center coordinates of each jump region are used as spatial positions to form a set of three-dimensional data points {(x_i, y_i, ρ_E_i)}. The spatial mapping uses the contour method to connect points with the same energy density to form a three-dimensional energy distribution map. The coordinate system is defined as a combination of geographical coordinates and energy density, with the x-y plane representing geographical position and the z-axis representing energy density value. The interpolation algorithm uses Kriging interpolation, with the center of the jump region as the interpolation node and the energy density ρ_E as the interpolation variable. The weight influence of the electrical distance is considered to ensure the continuity of the energy distribution. The contour interval is adaptively set according to the energy density range, with a smaller interval in high-density areas to improve resolution. The boundary condition processing considers the exchange power between the system and the external grid, with an open boundary allowing energy flow. The topological features of the energy distribution include peak points (energy sources), valley points (energy sinks), and saddle points (energy conversion points). The smoothness of the distribution profile reflects the stability of system operation, with sharp fluctuations indicating potential stability problems. The interpolation results form a continuous energy density field ρ_E(x, y), and the energy distribution profile is obtained by extracting the contour lines.

[0048] Energy concentration nodes are identified based on the energy distribution profile. From the continuous energy distribution profile ρ_E(x, y), the energy density value ρ_node = ρ_E(x_node, y_node) of each electrical node is obtained by sampling at its geographical location (x_node, y_node). The neighborhood average density ρ_avg is calculated by integrating the density within a radius r around the node. The energy concentration degree is defined as the ratio of the node energy density to the surrounding average density, C_node = ρ_node / ρ_avg. The concentration node screening criteria is set as C_node > 2.0 and duration exceeding 100 ms. The energy capacity evaluation of the node considers the thermal and dynamic stability limits of the equipment to prevent excessive energy concentration from causing equipment damage. The types of energy concentration nodes are classified as source type (continuous output energy), sink type (continuous energy absorption), and conversion type (energy transfer station). The energy exchange strength between nodes is calculated by the profile gradient: E_exchange = |∇ρ_E| × d_ij, where d_ij is the distance between nodes. The dynamic tracking algorithm monitors the migration path of the concentration node, and finds that the energy center will move along the path of least impedance after disturbance. The stability analysis of the concentration node is performed by the Lyapunov method, and unstable nodes need to be monitored.

[0049] An energy distribution pattern is constructed using the energy concentration nodes. The energy distribution pattern uses a network topology structure, with node size representing energy concentration degree and line thickness representing energy exchange strength. The pattern classification is based on the spatial characteristics of energy distribution: single-core mode (one dominant energy center), multi-core mode (multiple energy centers coexist), and dispersed mode (uniform energy distribution). The pattern feature vector contains key parameters such as the number of energy centers, energy distribution standard deviation, and energy exchange rate. The time-varying characteristics are described by a pattern transition matrix, which records the mode switching probability under different operating conditions. For example, after a large generator set trips, the system quickly transitions from a single-core mode to a multi-core mode, with the original energy center splitting into 3-4 secondary centers. This mode transition process lasts about 2-3 seconds. The mode stability index evaluates the ability of the mode to remain unchanged under small disturbances. Abnormal mode recognition is achieved by comparing with historical normal modes. The mode library is built to collect energy distribution patterns under typical operating scenarios, forming a complete energy distribution pattern.

[0050] In some embodiments, the phase correlation of the energy distribution pattern and the power transmission trajectory forms an impedance-power-energy coupling situation, which includes: extracting energy change timing from the energy distribution pattern; analyzing power fluctuation characteristics based on the power transmission trajectory to obtain power fluctuation timing; determining the correlation degree by phase comparison of the energy change timing and the power fluctuation timing; and forming an impedance-power-energy coupling situation according to the correlation degree.

[0051] The energy change time series is extracted from the energy distribution pattern. Time series extraction records the energy density curve E(t) of each energy concentration node over time. The sampling rate is consistent with the power data to ensure the accuracy of time series alignment. The time series features include change amplitude, change rate, dominant period, phase information and other key parameters. Trend separation uses the EMD method to decompose the energy time series into trend items, periodic items and random items. The periodic components are identified by spectral analysis, and the power frequency components reflect the steady-state characteristics, and the low-frequency components reflect the oscillation characteristics. The CUSUM algorithm is used to detect the mutation points of energy change to discover the transition of energy state in time. The statistical characteristics of time series comprehensively describe the probability distribution of energy change. The energy time series correlation analysis of adjacent nodes reveals the time delay characteristics of energy propagation. EMD decomposition and spectral analysis reveal the multi-time scale characteristics of energy change.

[0052] The power fluctuation characteristics are analyzed based on the power transmission trajectory to obtain the power fluctuation time series. From the active power P and reactive power Q curves in the power transmission trajectory, the power fluctuation amplitude is calculated as the deviation of the actual trajectory value from its moving average value. Using the 5-minute sliding window data of the power trajectory, the distribution characteristics of the fluctuation amplitude are statistically analyzed to reveal the power stability margin of the system. The frequency spectrum analysis of the power transmission trajectory identifies the main oscillation mode from the time domain signal of the trajectory, and distinguishes between mechanical and electrical oscillation and subsynchronous oscillation components. The phase space reconstruction of the power trajectory uses the delay coordinate method to reconstruct the dynamic characteristics of the system using the power sequence data in the trajectory. Based on the trajectory data, the cross-correlation function of power fluctuations at different nodes is calculated to determine the spatial correlation of fluctuations. Power reversal events are identified from the power transmission trajectory, recording the time and duration of the change in power flow direction in the trajectory. By analyzing the time difference of fluctuation peak values between adjacent nodes in the trajectory, the fluctuation propagation speed is calculated. Extreme fluctuation events are extracted from the power transmission trajectory as the focus of phase correlation analysis. Based on the fluctuation analysis of the power transmission trajectory, a complete power fluctuation time series is obtained.

[0053] The phase difference calculation uses Hilbert transform to extract the instantaneous phase, φ_diff(t) = φ_energy(t) - φ_power(t), where φ_energy and φ_power are the instantaneous phases of energy and power respectively. The sliding window correlation analysis calculates the correlation coefficient under different time delays to determine the optimal matching time delay of energy and power. The Granger causality test method is used for causality test to judge the causal relationship between power change and energy change. The phase-locked period is identified when the phase difference remains constant for more than a set time, and is marked as a locked state. Nonlinear correlation is measured by mutual information to capture complex relationships that linear correlation cannot reflect. The spatial distribution of correlation strength shows the influence of electrical distance on correlation degree. The time-varying correlation characteristics reflect the modulation effect of system operating state on coupling relationship. The phase correlation analysis results quantify the synchronization degree of power and energy.

[0054] The impedance-power-energy coupling situation is formed according to the correlation degree. The correlation degree directly determines the construction of the coupling strength matrix C: strong correlation (PSI>0.8) corresponds to matrix element C[i,j] taking value 0.8-1.0, and weak correlation (PSI<0.3) taking value 0.1-0.3. The time delay information τ_opt is embedded in the coupling dynamics equation, so that the system can predict the propagation delay between variables. The coupling situation is represented by a three-dimensional state space, with the coordinate axes being impedance Z, power P and energy E. The evolution trajectory of the situation forms a characteristic curve in the three-dimensional space, which is smooth and continuous in normal operation, and jumps or bifurcates in abnormal operation. Coupling mode recognition classifies the geometric features of the trajectory: spiral represents oscillatory coupling, divergent represents unstable coupling, and convergent represents stable coupling. For example, when a rapid 30% decrease in impedance is detected, according to the time delay relationship τ_opt=200ms, it is predicted that the power will be reversed after 200ms, so that the protection device can adjust the action strategy in advance. Through the establishment of the spatio-temporal coupling relationship by phase correlation, a complete impedance-power-energy coupling situation is finally formed.

[0055] In step S140, the protection adjustment time window is activated based on the impedance-power-energy coupling situation, the adjacent node communication protocol is established based on the protection adjustment time window, the adjacent state information is collected through the adjacent node communication protocol, and the cooperative distance protection potential field is constructed based on the adjacent state information.

[0056] Specifically, the protection regulation time window is activated based on the impedance-power-energy coupling situation. The activation condition of the time window is triggered by the key parameters in the coupling situation: when any element of the coupling strength matrix C exceeds 0.7, or the situation trajectory appears bifurcation characteristics, the time window is immediately opened. The length of the time window T_window is dynamically set according to the time delay parameter τ_opt of the coupling situation, T_window = 3 × max(τ_ZP, τ_PE, τ_ZE), which ensures to cover the complete coupling propagation process. The starting time t_start of the window is backtracked from the coupling situation anomaly detection point by 100 ms, capturing the initial stage of the anomaly. The sampling rate within the time window is increased to 5 times the normal value, reaching 500 Hz, improving the time resolution of state capture. The priority of protection regulation is determined according to the coupling mode: the time window triggered by the oscillatory coupling mode has the highest priority and needs to be responded immediately; the unstable coupling is second; and the stable coupling is the lowest. The spatial range of the time window is determined by the influence domain of the coupling situation, containing all node pairs with C[i,j]>0.5. Regular protection setting adjustment is prohibited within the window to avoid conflicts with collaborative regulation. The dynamic time window mechanism allows real-time adjustment of window parameters according to the evolution of the coupling situation. Through the accurate mapping of coupling parameters, the targeted protection regulation time window is activated.

[0057] In some embodiments, the establishing of the adjacent node communication protocol based on the protection regulation time window comprises: setting a communication time slot based on the protection regulation time window; allocating a node communication priority in the communication time slot; formulating an information exchange rule according to the node communication priority; and establishing an adjacent node communication protocol based on the information exchange rule.

[0058] The communication time slot is set based on the protection regulation time window. The time slot length is set to 1 / 100 of the time window, so that each time slot is about 5-10 ms, meeting the real-time transmission requirements of protection information. The time slot allocation adopts the TDMA mechanism, and each node transmits in the designated time slot and listens at other times. The first time slot is reserved for the key node that triggers the time window, ensuring that abnormal information is transmitted first. The synchronization reference of the time slot adopts a GPS clock with an accuracy of 1 μs. The protection time slot and the data time slot are allocated in a ratio of 7:3, giving priority to the transmission of protection information. The emergency time slot mechanism allows nodes that detect serious anomalies to preempt the nearest idle time slot. The time slot boundary is set with a protection interval to prevent signal overlap between adjacent time slots. The cyclic time slot structure ensures that each node has at least 3 transmission opportunities within the time window. The time slot configuration parameters can be dynamically adjusted according to the network size.

[0059] The communication priority of each node is assigned in each time slot. The priority is calculated by considering the position and state of the node in the coupling situation: P_comm=α×C_couple+β×D_risk+γ×N_neighbor, where C_couple is the maximum coupling strength of the node, D_risk is the risk level, N_neighbor is the number of neighboring nodes, and α, β, γ are weight coefficients. The energy concentration node in the coupling situation automatically obtains a high priority because its state change has a large influence range. The priority is divided into five levels, with level 1 being the highest and level 5 being the lowest, and each level corresponds to a different number of time slot assignments. The dynamic priority adjustment mechanism updates the priority table every 10 time slots according to real-time state changes. The priority inheritance rule ensures that the downstream nodes of high-priority nodes can also communicate in a timely manner. The priority arbitration uses a token passing method to avoid conflicts between nodes with the same priority. The priority promotion mechanism triggered by special events ensures a quick response to emergency situations.

[0060] Information exchange rules are formulated according to the communication priority of the nodes. High-priority nodes (levels 1-2) use a broadcast mode, with information being sent to all neighboring nodes simultaneously. Medium-priority nodes (level 3) use selective broadcasting, sending information only to risk-related nodes. Low-priority nodes (levels 4-5) use point-to-point communication to reduce network burden. The information format is defined as [Header|NodeID|Timestamp|Z|P|E|Status|CRC], with a total length of 128 bytes. Key information includes the real-time impedance, power, and energy values of the node, which are directly extracted from the coupling situation monitoring. The status field encodes the operating mode, protection action state, and abnormal flag of the node. The information update frequency is associated with the priority: level 1 nodes update every time slot, and level 5 nodes update every 5 time slots. The retransmission mechanism is enabled for key information, with a maximum of 3 retransmissions. Information integrity is verified by CRC32, with an error rate controlled below 10^-6.

[0061] Based on the information exchange rules, a communication protocol for neighboring nodes is established. The protocol uses a master-slave structure, with time window triggered nodes acting as temporary masters to coordinate the entire communication process. The handshake process includes: the master node broadcasts a SYNC frame, the slave node replies with an ACK frame, the master node assigns time slots, and the slave node confirms. The protocol stack is divided into the physical layer (fiber communication), the link layer (time slot management), and the application layer (protection information exchange). The state machine design includes four states: idle, synchronization, active, and abnormal, with state transitions driven by time windows and priority. The timeout mechanism is set to 50ms for the handshake and 100ms for data, after which it automatically downgrades to a backup communication mode. Protocol efficiency optimization is achieved through information aggregation, with nodes with similar states sharing time slots. Compatibility design supports interoperability with existing protection communication protocols (such as IEC61850). The security mechanism includes node authentication and information encryption to prevent malicious interference. The adaptive characteristics of the protocol dynamically adjust parameters according to network load.

[0062] The adjacent state information is collected through the adjacent node communication protocol. The information collection range includes direct adjacent (electrical distance is 1) and secondary adjacent (electrical distance is 2) nodes. The content of collection is to extract the impedance value Z_neighbor, the power value P_neighbor and the energy density E_neighbor from the communication frame. The real-time requirement is that all adjacent node information is completed within 20 ms in one round of collection. The data preprocessing includes bad data identification and elimination, and the 3σ criterion is used to detect abnormal values. The information fusion adopts a weighted average method, and the weight is related to the communication delay and signal quality. The historical information cache retains the data of the last 100 ms for trend analysis. The differentiated collection strategy increases the collection frequency to once per time slot for high-risk adjacent nodes. After the collection is completed, the adjacent state information matrix S_neighbor[i,j] is formed, which contains the state parameters of all adjacent node pairs. The information quality evaluation is carried out through three dimensions of integrity, timeliness and consistency.

[0063] In some embodiments, the constructing a cooperative distance protection potential field based on the adjacent state information comprises: analyzing the node protection capability by using the adjacent state information, and extracting a protection capability distribution; performing potential energy mapping on the protection capability distribution to obtain a potential energy distribution; determining a protection force field direction based on the potential energy distribution; and constructing a cooperative distance protection potential field according to the protection force field direction.

[0064] The node protection capability is analyzed by using the adjacent state information, and a protection capability distribution is extracted. The protection capability is defined as the comprehensive capability of the node to resist mis-trip, and the calculation formula is P_protect=(Z_margin / Z_set)×(1-Load_ratio)×Comm_quality, wherein Z_margin is a protection margin, which is obtained by the difference between the current impedance value of the adjacent node and the original setting impedance value of each node, Z_set is the set setting value of the node protection device, Load_ratio is the load ratio, which is calculated by the ratio of the real-time power value in the adjacent state information to the node rated capacity, and Comm_quality is the communication quality index. The state difference ΔS=|S_node-S_neighbor| of the adjacent node reflects the protection pressure, and the greater the difference, the higher the protection capability requirement. The capability evaluation considers the static capability (device performance, setting margin) and the dynamic capability (real-time load, communication state). The spatial distribution characteristics are obtained by marking the protection capability value P_protect of each node on the network topology. The capability gradient calculation reveals the spatial variation trend of the capability. The adjacent cooperative capability is evaluated by the product of the capability of the node pair. The weak link identification marks the nodes with insufficient protection capability. The time-varying characteristic tracking shows that the protection capability generally decreases during the heavy load period.

[0065] The potential energy distribution is obtained by potential energy mapping of the protection capability distribution. The potential energy mapping adopts electrostatic field analogy to convert the protection capability P protect into potential energy value: U(x, y) = -k∫(P protect / r)dr, where k is the mapping coefficient, and r is the spatial distance. High protection capability corresponds to low potential energy (stable area), and low protection capability corresponds to high potential energy (dangerous area). The boundary conditions of the potential energy field are that the system boundary is set as a zero potential energy reference point, and the fault point is set as a maximum potential energy. The spatial discretization adopts the finite element method to divide the protection area into triangular elements, and the potential energy is linearly interpolated in each element. The potential energy gradient field indicates the natural trend from the high-risk area to the low-risk area. The equipotential line drawing reveals the topological structure of the potential energy distribution, and the closed equipotential line represents the local extreme point. The potential energy well (local minimum) corresponds to the safe area with strong protection capability, and the potential energy peak (local maximum) corresponds to the dangerous area prone to mis-trip. The mapping result forms a continuous potential energy distribution field.

[0066] The protection force field direction is determined based on the potential energy distribution. The protection force field is defined as the negative gradient of the potential energy: F protect = -∇U, pointing to the direction of the fastest potential energy reduction. The force field strength |F protect| reflects the urgency of protection adjustment, and the area with high strength needs to respond quickly. The direction field calculation is performed at each node position to obtain a vector field. Streamline tracing starts from the high potential energy point and advances along the force field direction to form the preferred path of protection action. Vortex detection identifies the circulation structure in the force field, and these areas may cause protection action circulation. The divergence analysis shows the source and sink distribution characteristics of the protection capability. The time evolution of the force field is obtained by continuously updating the potential energy distribution, which reveals the dynamic changes of protection demand. Singularity analysis finds the equilibrium points where the force field is zero, and the stability of these points determines the protection characteristics of the system.

[0067] The cooperative distance protection potential field is constructed according to the direction of the protection force field. The cooperative potential field integrates the single-node force field to form a unified protection adjustment field of the whole network: Φ coop =∑F i ×W i, wherein F i is the force field vector of node i, and W i is the cooperative weight of node i. The weight calculation considers the electrical centrality and the fault propagation influence degree of the node, and the weight of the central node is higher. The range of the potential field is determined by the field strength attenuation, and the field strength attenuates according to the inverse square law of distance. The cooperative mechanism makes the protection actions of adjacent nodes related to each other, and the adjustment of one node will affect the potential field distribution of surrounding nodes. The field superposition principle allows the fields generated by multiple risk sources to be vector superimposed. For example, when S130 detects that the impedance-power-energy coupling situation of a certain 500kV node appears spiral oscillation (coupling strength C=0.85), the time window of S140 is immediately activated (T_window=600ms), and the communication protocol collects the state information of the adjacent 6 nodes within 20ms to show that the protection ability is reduced. The cooperative potential field immediately forms a gradient distribution in the region: the potential energy of the oscillation center node is the highest, the force field vectors of the surrounding nodes point to the outside, and the distance protection of each node is automatically guided to retreat (5%-20%) according to the field strength grading, and at the same time, the high-priority node updates the potential field parameters every 5ms to ensure that no mis-trip event occurs during oscillation. The dynamic potential field update cycle is synchronized with the communication time slot to ensure real-time performance. The establishment of the cooperative distance protection potential field enables each node in the bay ring to intelligently adjust based on the global protection situation and the real-time state of adjacent nodes, realizing the transformation from independent protection to cooperative protection.

[0068] Step S150, performing protection range gradient scanning in the cooperative distance protection potential field, constructing an inter-node cooperation network based on the scanning result, performing energy distribution analysis on the cooperation network to determine the cooperative coverage scope, and using vector superposition to perform multi-dimensional synthesis on the scope, the key section and the impedance-power-energy coupling situation to generate a multi-dimensional protection decision domain.

[0069] Specifically, the protection range gradient scanning is performed in the cooperative distance protection potential field Φ coop(x, y, z). The gradient scanning algorithm proceeds along the normal direction of the equipotential surface of the potential field, and the scanning step is one-tenth of the radius of the protection zone to ensure the balance between accuracy and efficiency. The scanning path starts from the local extreme point of the potential field and advances along the direction with the maximum gradient, and records the boundary changes of the protection range. The gradient calculation uses the central difference method: ∇Φ = (Φ(x+h)-Φ(x-h)) / 2h, where h is the difference step. The scanning covers the entire interval ring topology, and the gradient vector and scalar value of the potential field are extracted at each node position. Boundary identification is achieved through gradient jump detection, and when the gradient value jumps by more than 50%, it is marked as a protection boundary. The scanning density is automatically increased in areas with intense potential field changes to ensure that all key features are captured. Three-dimensional scanning considers the gradient changes in impedance, power, and energy dimensions to form a complete gradient field map. The scanning results include the gradient vector, protection range radius, boundary curvature, and other parameters of each node. The dynamic scanning mechanism updates every 100 ms to track the time-varying characteristics of the potential field. Gradient scanning reveals the spatial distribution characteristics and evolution laws of the cooperative protection potential field.

[0070] In some embodiments, the construction of the inter-node cooperative network based on the scanning results includes: implementing weak link identification on the gradient scanning results to obtain protection coverage weak links; establishing an initial cooperative relationship based on the protection coverage weak links to construct a basic inter-node cooperative network; performing cooperative strength evaluation based on the operation effect of the basic inter-node cooperative network to obtain a cooperative effect index; and dynamically adjusting the initial cooperative relationship based on the cooperative effect index to form an adaptive inter-node cooperative network.

[0071] Weak link identification is performed on the gradient scanning results to obtain protection coverage weak links. Weak links are defined as areas with gradient values less than a threshold and protection range overlap less than 30%. The identification algorithm iterates through all scanning points, and calculates the local protection coverage ratio as the ratio of the covered area to the total area. Coverage blind spots are identified through Boolean operations of multiple node protection ranges, and areas not covered by any protection circle are marked as blind spots. Gradient fault detection finds the positions of gradient field discontinuity, which often correspond to weak points of protection coordination. Boundary vulnerability analysis evaluates the sensitivity of protection boundaries to disturbances, and high-sensitivity boundaries are listed as weak links. Spatial distribution statistics show that weak links are mainly concentrated in: sparse node areas, multi-voltage level junctions, and middle sections of long-distance lines. Weakness is quantified by considering coverage deficiency, gradient anomaly, and boundary vulnerability. The identification results form a weak link list and a spatial distribution map.

[0072] The initial cooperation relationship is established based on the weak link of protection coverage, and a cooperation network between basic nodes is constructed. The initial cooperation strategy preferentially connects the strong nodes on both sides of the weak link to form a "strong-weak-strong" support structure. The cooperation pair selection is based on the complementary principle: nodes with small gradients cooperate with nodes with large gradients, and areas with insufficient coverage cooperate with areas with redundant coverage. The cooperation path planning avoids high-risk areas and selects a path with stable potential field to establish a connection. The initial network uses a minimum connected graph to ensure that each weak link has at least two cooperation supports. The cooperation bandwidth allocation is determined according to the weakness, and more cooperation resources are allocated to severely weak links. Network initialization includes: node cooperation capability evaluation, cooperation pair matching, path establishment, parameter configuration. The cooperation protocol defines the information exchange format, update frequency, and abnormal handling mechanism. The redundancy design of the basic network ensures that a single point failure will not cause cooperation interruption.

[0073] The cooperation strength is evaluated based on the running effect of the cooperation network between basic nodes, and the cooperation effect index is obtained. The effect evaluation tests the response ability of the network by injecting simulated disturbances and records the improvement of the protection of weak links. The cooperation strength index S_coop = ΔC_coverage × R_time × (1-P_false), where ΔC_coverage is the difference between the coverage rate after cooperation and the coverage rate before cooperation, R_time is the reciprocal of the cooperation response time, and P_false is the probability of false action in the cooperation process, which is obtained through historical statistical data. Performance tests include: single point failure test, cascading failure test, oscillation disturbance test, extreme scenario test. The evaluation dimensions cover: coverage integrity (eliminate blind area), response rapidity (cooperation delay), action accuracy (misoperation rate), resource efficiency (communication overhead). Bottleneck identification finds out the key factors that restrict the cooperation effect, such as communication delay and coordination conflict. Time series analysis of effect indicators reveals the stability of cooperation performance. The evaluation results provide quantitative basis for the optimization and adjustment of cooperation relationship.

[0074] The initial cooperation relationship is dynamically adjusted based on the cooperation effect index to form an adaptive inter-node cooperation network. The adjustment strategy is based on the feedback of the effect index: the cooperation pair with S_coop<0.5 is re-matched or the connection is enhanced; the resource allocation of the cooperation pair with S_coop>0.8 can be appropriately reduced. The adaptive algorithm uses reinforcement learning method to continuously optimize the network structure with the goal of maximizing the cooperation effect. The dynamic weight of the cooperation relationship is adjusted according to the degree of improvement of the effect to realize adaptive optimization. The evolution of the network topology allows the addition of new cooperation edges or the deletion of inefficient connections to maintain the vitality of the network. The cooperation mode switching mechanism switches between different modes according to the system state: normal mode, enhanced mode, and emergency mode. Parameter adaptation includes real-time adjustment of cooperation bandwidth, update frequency, and trigger threshold. Convergence monitoring ensures stable convergence of the adjustment process to avoid oscillation. The final adaptive cooperation network can autonomously adjust the structure and parameters according to the system state and weak link distribution.

[0075] The energy distribution analysis of the cooperation network determines the scope of the synergistic coverage. The energy analysis uses the aforementioned energy distribution mode to reorganize the energy density values of each node in S130 according to the connection relationship of the cooperation network. The energy transmission of the cooperation edge is calculated through the energy density difference between the connected nodes. The total energy of the network is the sum of the node energy and the transmission energy, which reflects the energy state of the cooperation network. The energy transmission efficiency of the cooperation edge reflects the energy cost of cooperation, and high efficiency indicates low cooperation cost. The energy flow analysis uses Kirchhoff's law to ensure the conservation of network energy. The scope of synergistic coverage is defined as the spatial range that can be effectively controlled by the cooperation network, and the boundary is determined by the energy density contour surface. The scope radius r_coverage=√(E_total / π×ρ_threshold), where E_total is the total energy and ρ_threshold is the energy density threshold for effective control, which is the average value of the energy density of all nodes in the cooperation network. The multi-center scope is calculated by the superposition effect of the energy centers, considering the gain brought by cooperation. The time-varying characteristics of the scope reflect the dynamic changes of the energy distribution. The boundary stability analysis ensures that the scope does not fluctuate sharply. The scope of synergistic coverage forms a three-dimensional spatial region with energy density as the boundary, clearly defining the effective control range of the cooperation network.

[0076] In some embodiments, the multi-dimensional synthesis of the scope, the critical section and the impedance-power-energy coupling situation by vector superposition generates a multi-dimensional protection decision domain, including: generating a spatial vector based on the collaborative coverage scope, generating a weight matrix based on the critical section, and performing a first layer vector synthesis based on the spatial vector and the weight matrix to obtain a primary synthesis result; performing consistency verification on the primary synthesis result, and entering a second layer synthesis process based on a pass state of the verification; constructing a time dimension tensor based on the impedance-power-energy coupling situation, and performing secondary vector superposition based on the time dimension tensor and the primary synthesis result to obtain a candidate synthesis result; performing integrity inspection based on the candidate synthesis result, and generating a multi-dimensional protection decision domain verified by layering according to the inspection result.

[0077] A spatial vector is generated based on the collaborative coverage scope, a weight matrix is generated based on the aforementioned critical section, and a first layer vector synthesis is performed to obtain a primary synthesis result. The spatial vector is constructed by extracting the boundary of the collaborative coverage scope, discretizing the scope boundary into N sampling points, and each point containing three-dimensional coordinates, energy density and potential field phase information. The vectorization process uses principal component analysis for dimension reduction, retaining more than 95% of the spatial information to form a compact feature representation. The generation of the critical section weight matrix uses the aforementioned risk degree D_risk value to construct a weight distribution relationship. The matrix elements reflect the mutual influence between sections, considering the electrical distance and the risk degree difference. The formula of the first layer vector synthesis is C_primary=V_space×W_section, where C_primary is the primary synthesis result, V_space is the spatial vector generated based on the collaborative coverage scope, and W_section is the weight matrix constructed based on the risk degree of the critical section, realizing the fusion of spatial information and risk weight. The synthesis process maintains physical meaning and ensures that the result is interpretable. The synthesis algorithm uses block matrix operation to improve the processing efficiency of large-scale data. The primary synthesis result forms a three-dimensional data field, and each spatial point contains position information and risk weight. The numerical stability in the operation process is monitored and guaranteed by the condition number. After the synthesis is completed, the primary result of the fusion of spatial distribution and risk characteristics is obtained.

[0078] The consistency of the primary synthesis results is verified to ensure the validity and reasonableness of the data. The verification framework includes three levels of topological consistency, numerical consistency and physical consistency. Topological consistency checks whether the spatial structure after synthesis maintains connectivity, and confirms that there are no isolated nodes through graph traversal algorithm, and the number of connected components remains unchanged. Numerical consistency verifies the reasonableness of the value range, and eliminates abnormal values and singular points beyond the 3σ range. Physical consistency ensures that the synthesis results meet the basic constraints of the power system, such as power balance error less than threshold, voltage amplitude within reasonable range. The inconsistent processing adopts iterative correction method, defines consistency loss function and minimizes through optimization algorithm, and iterates at most 5 times. The adaptive step is adopted in the correction process to prevent over-adjustment from damaging the original features. The verification passes the standard set for all consistency indicators greater than 0.9. The verification record saves the results of each check, which is convenient for problem tracing. The primary synthesis results that pass the verification have the conditions to enter the next layer of processing.

[0079] Based on the impedance-power-energy coupling situation, a time dimension tensor is constructed, and the secondary vector superposition is carried out with the primary synthesis results that pass the verification. The time dimension tensor extracts the time series of impedance, power and energy from the coupling situation, and organizes them into a multi-dimensional array structure T[i,j,k]. The time sampling interval is set to 5ms, which matches the time scale of protection action, to ensure the capture of key dynamic process. Tensor preprocessing includes time alignment, noise filtering and trend extraction, using interpolation and filtering techniques. The coupling relationship is embedded in the tensor structure through the coupling strength matrix C mentioned above. The secondary superposition adopts a spatio-temporal fusion algorithm, and the formula is S_candidate=Conv(C_primary,T_couple), where S_candidate is the candidate synthesis result, Conv represents convolution operation, C_primary is the primary synthesis result, and T_couple is the time tensor. The convolution kernel size is 5x5x5, and the causality is considered in the design to ensure that future information does not affect the current decision. The time weight adopts exponential decay, which makes the recent history have greater influence. The spatial neighborhood adjusts the influence range through Gaussian function. Multi-scale processing is carried out at different spatio-temporal resolutions, and then the results are fused. The candidate synthesis result forms a four-dimensional spatio-temporal data structure.

[0080] Based on the candidate synthesis results, integrity verification is performed to generate a multi-dimensional protection decision domain. The integrity verification ensures complete coverage, time continuity, and full dimension. The spatial coverage checks whether there is a protection blind area, and the time continuity verifies whether there is a breakpoint. The causal integrity ensures the time sequence rationality of the decision logic. After the verification passes, a multi-dimensional protection decision domain D_domain=[x, y, z, t, a] is constructed, where x, y, and z are the three-dimensional spatial coordinates (longitude, latitude, and voltage level) of the nodes, t is the time dimension (time after fault occurrence), and a is the action dimension (including protection action type, setting value adjustment amount, and action time sequence). The action space specifically includes: protection setting value adjustment range, action delay selection, and action type setting (trip, alarm, and lockout). Each space-time point (x, y, z, t) is mapped to the optimal protection action scheme a. The feasibility of the decision is verified by device constraints and coordination relationships. For example, when a substation detects strong coupling oscillation and is located in a high-risk section, the multi-dimensional protection decision domain immediately generates a comprehensive action scheme according to the space-time coordinates of the point: the station retreats appropriately, the adjacent stations in the collaborative network adjust synchronously, a gradient defense system is formed, and the action time sequence is executed according to the predetermined interval to avoid protection action conflicts. Through gradient scanning, collaborative network construction, and multi-dimensional information fusion, the multi-dimensional protection decision domain realizes intelligent mapping from dispersed local information to collaborative global optimal decision, providing a time-space integrated protection decision capability for large-distance interval rings.

[0081] In step S160, a global search is performed on the multi-dimensional protection decision domain, and the optimal balance point of protection reliability and selectivity is located from the search results. A hierarchical protection setting sequence is generated based on the balance point, a node coordination adjustment strategy is formulated based on the collaborative network, and the hierarchical protection setting sequence is optimized through the adjustment strategy to output the optimal protection configuration.

[0082] Specifically, a global search is implemented on the multi-dimensional protection decision space. The search algorithm adopts an adaptive grid method, and the initial grid size is dynamically set according to the dimensions of the multi-dimensional protection decision space. The grid is automatically refined in the high gradient area. The search objective function is defined as F_obj = a x R_reliability - b x S_selectivity, where R_reliability is a protection reliability index, S_selectivity is a selectivity index, and a and b are weight coefficients. The reliability index is evaluated by the fault removal probability and the action speed, and the selectivity index is quantified by the misoperation probability and the influence range. The search process starts from the center area of the multi-dimensional protection decision space and gradually covers the entire space by adopting a spiral expansion strategy. Each search point (x, y, z, t, a) corresponds to a specific protection configuration scheme, and the objective function value thereof is calculated. The search acceleration is achieved by parallel computing, and the multi-dimensional protection decision space is divided into multiple sub-regions for simultaneous search. The constraint conditions include protection coordination relationship, device action capability, system stability boundary, etc., and the points that do not meet the constraints are directly eliminated. The search process records the top 100 optimal solutions to form a candidate scheme set. The dynamic search strategy adjusts the search direction and density according to the distribution of the optimal solutions found. The complete distribution map of the objective function is obtained after the global search is completed.

[0083] The optimal balance point of protection reliability and selectivity is located from the search results. The balance point positioning adopts Pareto frontier analysis to find the non-inferior solution set in the reliability-selectivity two-dimensional space. Each point on the Pareto frontier represents a trade-off scheme, and it is impossible to improve both indicators simultaneously. The balance point selection considers the actual needs of the system: important hub stations emphasize reliability, and general distribution stations emphasize selectivity. Multi-objective decision-making adopts the analytic hierarchy process to convert qualitative preferences into quantitative weights. Sensitivity analysis evaluates the stability of the balance point to parameter changes and selects a scheme with strong robustness. The time-varying characteristics of the balance point are obtained through analysis on different time sections to adapt to changes in system operation mode. During the fault-prone period such as thunderstorm season, the balance point automatically tilts towards reliability; during system maintenance, the balance point tilts towards selectivity to avoid misoperation of healthy devices. The spatial distribution characteristics show that the optimal balance points in different regions are different, reflecting the influence of network structure. The final determined balance point achieves a good trade-off between reliability and selectivity.

[0084] The optimal balance point is used to generate the hierarchical protection setting sequence. The hierarchical structure is divided according to voltage level and protection importance: the first layer is 500 kV main protection, the second layer is 220 kV backup protection, and the third layer is 110 kV and below distribution protection. According to the coordinate position of the optimal balance point in the multi-dimensional decision domain, the setting sequence is generated by backtracking to the corresponding action dimension a, and the setting value configuration of each layer is extracted from the action dimension a corresponding to the balance point. The sequencing process considers the timing coordination of protection actions, and the delay of the upper-level protection must be greater than the lower-level protection. The setting value calculation formula is Z_set[i]=Z_base×(1+k_layer×(i-1)), where Z_set[i] is the setting impedance of the i-th layer, Z_base is the base impedance, k_layer is the hierarchical coefficient, and i is the layer number. The time setting follows the step principle, and the time difference between adjacent layers is kept within a reasonable range. The setting of the directional element considers the flow distribution to ensure accurate identification of positive direction faults. The setting range of the distance element covers 80%-85% of the protected line to avoid excessive extension. When double-circuit lines are running in parallel, the setting range is appropriately contracted; when single-circuit lines are running, the setting range can be appropriately extended. The hierarchical coordination is achieved through the progressive relationship of setting values, and the action value of the lower-level protection is always less than the upper-level protection. The setting sequence contains multiple sets of setting values under normal, maintenance, and special operating modes.

[0085] The node coordination strategy is developed based on the cooperation network. The coordination strategy uses the topology structure and cooperation strength matrix W_coop to assign adjustment tasks to each node. The task allocation algorithm considers the cooperation ability, current load, and adjustment margin of the node. Core nodes bear the main adjustment responsibility, and edge nodes provide auxiliary support. The coordination modes include: synchronous adjustment (all nodes act simultaneously), cascade adjustment (act sequentially in sequence), and grouping adjustment (synchronous within the group and cascaded between groups). The adjustment amplitude is determined according to the value of W_coop[i, j], and a small setting value difference is maintained between strongly cooperative nodes (W_coop>0.8). Time sequence coordination ensures that the adjustment action will not cause new disturbances, and soft switching technology is used for smooth transition. The conflict resolution mechanism handles the situation where multiple nodes request adjustment at the same time, and arbitration is performed according to priority. Dynamic coordination allows adjustment of the coordination relationship according to real-time state to enhance the adaptability of the system.

[0086] The optimal protection configuration is output by adjusting the strategy to optimize the hierarchical protection setting sequence. The optimization process embeds the coordination strategy into the hierarchical setting sequence, realizing the unity of local and global. The optimization goal maximizes the coordination benefit on the basis of maintaining the hierarchical structure. The constraint conditions include: protection coordination margin, device adjustment capacity, communication delay limit, etc. The optimization algorithm uses the particle swarm algorithm, and each possible configuration scheme is regarded as a particle. The fitness function of the particle considers the protection performance and coordination effect. In the iteration process, the particle updates its position according to the individual optimal and group optimal. The convergence criterion is set as the improvement of fitness less than a threshold or reaching the maximum iteration number. The optimization result includes the complete configuration of the protection setting value, action time limit, coordination parameter, etc. of each node. The implementation of the configuration is completed through remote setting or local download. For example, in a certain 500kV interval ring, the optimized protection configuration realizes the gradient distribution of the hierarchical setting value: the main protection covers 85% of the line, the backup protection of the adjacent line automatically retreats, and the sensitivity of the cooperating node is dynamically adjusted according to the real-time coupling strength, forming a three-dimensional defense system that cooperates in space and acts in order in time. Finally, the optimal protection configuration suitable for large-distance interval ring is obtained.

[0087] In step S170, the dynamic impedance state of the interval ring is monitored in real time after the optimal protection configuration is executed, the protection state transition critical point is identified based on the dynamic impedance state, and the node coordination strategy is triggered based on the protection state transition critical point, thereby completing the intelligent anti-malfunction trip protection.

[0088] Specifically, the optimal protection configuration is issued to each node protection device and executed, and the dynamic impedance state of each node of the interval ring is monitored in real time. The monitoring uses the deployed PMU device to collect impedance data at a density of 50 sampling points per cycle to form a dynamic impedance cloud map. The dynamic impedance state Z_dynamic is defined as the probability density distribution of the impedance measurement value on the R-X plane, reflecting the statistical characteristics of the system operating state. The real-time monitoring uses a sliding time window method with a window length of 200ms and a step of 50ms to ensure the capture of fast-changing processes. The data flow processing architecture supports real-time analysis of high-speed data, and uses stream computing technology to reduce processing delay. The abnormality detection algorithm compares the current dynamic impedance state with the normal operation benchmark, and triggers deep analysis when the deviation exceeds the threshold. The morphological characteristics of the dynamic impedance state include: center position (reflecting the steady-state operating point), dispersion degree (reflecting the disturbance level), and skewness (reflecting the asymmetry). The correlation analysis of the dynamic impedance states of multiple nodes identifies the systematic change trend and distinguishes between local disturbance and global anomaly. The monitoring interface displays the impedance trajectory and dynamic impedance state evolution of each node in real time, providing intuitive state perception for the operating personnel.

[0089] In some embodiments, the identifying the protection state transition critical point based on the dynamic impedance state comprises: constructing a density gradient distribution by the dynamic impedance state; searching for a gradient mutation position in the density gradient distribution; and performing stability evaluation on the gradient mutation position to determine the protection state transition critical point.

[0090] A density gradient distribution is constructed by the dynamic impedance state of the interval ring. The density calculation uses a kernel density estimation method to estimate the probability density of impedance occurrence at each grid point in the R-X plane. The Gaussian kernel is selected as the kernel function, and the bandwidth parameter is adaptively adjusted according to the number of samples. The density gradient ∇ρ=(∂ρ / ∂R, ∂ρ / ∂X) represents the spatial variation rate of the density, where ρ is the probability density, R is the real part of the impedance, and X is the imaginary part of the impedance. The construction of the gradient field covers the entire impedance plane of the concerned area, and the grid resolution is dynamically adjusted according to the data density. The gradient analysis of multiple time scales calculates the instantaneous gradient (reflecting the rapid change) and the average gradient (reflecting the trend) respectively. The direction of the gradient vector indicates the dominant direction of impedance migration, and the amplitude reflects the degree of change. The contour line of the gradient shows the topological structure of the density change, and the closed contour line may contain the critical point. The time evolution of the gradient field shows the dynamic characteristics of the system state by continuous updating.

[0091] The gradient mutation position is searched in the density gradient distribution. The mutation detection algorithm scans the gradient field to identify the position where the gradient value changes sharply. The mutation criterion is defined as |∇ρ(i+1)-∇ρ(i)| / |∇ρ(i)|>δ, where δ is the mutation threshold and i is the position index. The search strategy uses a multi-resolution method to quickly locate in a coarse grid and accurately mark in a fine grid. Cluster analysis of the mutation position merges adjacent mutation points to form a mutation region. The quantification of mutation intensity considers the gradient jump amplitude and the influence range. Directional analysis distinguishes radial mutations (along the radial direction) and tangential mutations (along the circumferential direction), which correspond to different physical mechanisms. The time persistence of the mutation distinguishes transient mutations and persistent mutations, the latter of which is more likely to cause state transition. Spatial correlation analysis finds that there is a propagation relationship between certain mutation positions. The search results form a set of mutation positions and their characteristic parameters.

[0092] The stability of the gradient mutation positions is evaluated to determine the critical points of the protection state transition. The stability evaluation analyzes the impedance trajectory behavior near the mutation positions to determine whether the system is in a critical state. The Lyapunov exponent calculation evaluates the divergence characteristics of the trajectory, with positive values indicating instability. The small perturbation test observes the response of the mutation position to small changes, and positions with high sensitivity may be critical points. The attraction domain analysis determines the influence range of each stable state, and the critical point is located at the boundary of the attraction domain. The bifurcation feature identification observes the state mutation by continuous variation of the parameters to capture the position of the bifurcation point. The recurrence plot analysis of the time series reveals the periodicity and chaos characteristics of the system. The multi-index comprehensive score S_critical considers the Lyapunov exponent, sensitivity, and bifurcation characteristics to quantify the criticality of each mutation position. The threshold setting determines the positions with S_critical>S_th as the transition critical points, where S_th is the criticality threshold. The physical meaning of the critical point corresponds to the boundary of the protection action, and mis-trip is prone to occur at this point.

[0093] The transition critical point is identified to trigger the node coordination regulation strategy. The trigger mechanism monitors the distance between the system state and the critical point, and initiates the regulation when the impedance trajectory approaches the critical point. The trigger conditions include: the distance is less than the safety margin, the approach speed exceeds the threshold, and the oscillation near the critical point, etc. The regulation strategy calls the coordination scheme formulated in S160, and selects the appropriate regulation mode according to the position and characteristics of the current critical point. The emergency assessment determines the response speed of the regulation, and the high-risk critical point triggers a fast response. The regulation instructions are distributed to the relevant nodes through the communication channel established in S140, ensuring the synchronization of the coordinated action. The regulation amplitude is dynamically determined according to the distance between the system state and the critical point, and the closer the distance, the greater the regulation. Multi-node coordination is achieved through predetermined cooperation, with strong cooperation nodes synchronized and weak cooperation nodes sequentially regulated. The real-time feedback of the regulation effect is reflected by the change of the impedance trajectory, and successful regulation makes the trajectory away from the critical point. The standby strategy is automatically activated when the main regulation fails, ensuring system safety. The intelligent anti-malfunction trip protection system comprehensively utilizes all the achievements of the previous steps: the risk transmission matrix of S110 provides fault propagation prediction, the key section positioning of S120 locates high-risk areas, the coupling situation of S130 reveals multi-dimensional association, the coordination potential field of S140 guides protection coordination, the multi-dimensional protection decision domain of S150 provides optimization scheme, and the optimal configuration of S160 realizes system-level optimization. Real-time monitoring and dynamic regulation form a closed-loop control, continuously optimizing protection performance. For example, when a single-phase ground fault occurs on a 500kV line, the impedance of the adjacent line is quickly detected to approach the transition critical point, triggering the coordinated regulation: the fault line protection normally operates at both ends, the distance protection of the adjacent 3 lines respectively retreats different amplitudes according to the coupling strength, and the sensitivity of the directional element is adjusted, successfully avoiding the malfunction trip caused by zero sequence mutual inductance. The intelligent anti-malfunction trip protection realizes the transformation from passive response to active defense of the large distance interval ring protection through multi-dimensional information fusion, global coordination optimization and dynamic self-adaptive regulation, and completes the intelligent anti-malfunction trip protection.

[0094] In order to perform the large distance interval ring distance protection anti-malfunction trip method corresponding to the above-mentioned method embodiment, to realize the corresponding functions and technical effects. Referring to Figure 2 , Figure 2 The structure block diagram of the large distance interval ring distance protection anti-malfunction trip device 200 provided by the embodiment of the application is shown. For ease of illustration, only the part related to the embodiment is shown, and the large distance interval ring distance protection anti-malfunction trip device 200 provided by the embodiment of the application comprises:

[0095] The fault processing module 201 is configured to collect fault propagation records of each circuit breaker node in the large distance interval ring, and perform structured processing on the fault propagation records to construct a malfunction trip risk transmission matrix.

[0096] The spectrum analysis module 202 is configured to perform fault impact path tracking by using the mis-trip risk transmission matrix, perform wavelet decomposition on the fault impact path to obtain a mutation spectrum feature, and determine a key section requiring preset protection margin according to the mutation spectrum feature.

[0097] The energy situation module 203 is configured to extract impedance aggregation states and power transmission trajectories of interval ring nodes in the key section, locate an impedance jump region from the impedance aggregation states, construct an energy distribution mode based on the impedance jump region, and form an impedance-power-energy coupling situation by phase correlation between the energy distribution mode and the power transmission trajectory.

[0098] The potential field construction module 204 is configured to activate a protection adjustment time window based on the impedance-power-energy coupling situation, establish an adjacent node communication protocol based on the protection adjustment time window, collect adjacent state information through the adjacent node communication protocol, and construct a collaborative distance protection potential field based on the adjacent state information.

[0099] The decision generation module 205 is configured to perform protection range gradient scanning in the collaborative distance protection potential field, construct an inter-node collaboration network based on the scanning result, perform energy distribution analysis on the collaboration network to determine a collaborative coverage scope, and perform multi-dimensional synthesis on the scope, the key section and the impedance-power-energy coupling situation by vector superposition to generate a multi-dimensional protection decision domain.

[0100] The configuration optimization module 206 is configured to perform global search on the multi-dimensional protection decision domain, locate an optimal balance point of protection reliability and selectivity from the search result, generate a hierarchical protection setting sequence based on the balance point, develop a node collaborative adjustment strategy based on the collaboration network, and output an optimal protection configuration by optimizing the hierarchical protection setting sequence through the adjustment strategy.

[0101] The dynamic adjustment module 207 is configured to monitor a dynamic impedance state of an interval ring in real time after executing the optimal protection configuration, identify a protection state transition critical point based on the dynamic impedance state, trigger the node collaborative adjustment strategy based on the protection state transition critical point, and complete intelligent anti-mis-trip protection.

[0102] The long-distance interval ring distance protection anti-mis-trip device 200 described above can implement the long-distance interval ring distance protection anti-mis-trip method described in the method embodiment. The optional items in the method embodiment are also applicable to this embodiment, and will not be described in detail here. The remaining content of the embodiment of the present application can refer to the content of the method embodiment, and will not be described in detail in this embodiment.

[0103] The above embodiments are also not exhaustive enumeration based on the present application, in addition to which, there can be a plurality of other embodiments not listed. Any substitution and improvement made without violating the concept of the present application is within the scope of protection of the present application.

Claims

1. A method for preventing false tripping in a large-distance interval ring protection system, characterized in that, include: Fault propagation records of each circuit breaker node in a long-distance interval ring are collected, and the fault propagation records are structured to construct a false trip risk transmission matrix; The fault impact path is traced using the fault trip risk transmission matrix. Wavelet decomposition is performed on the fault impact path to obtain abrupt spectral features. Based on the abrupt spectral features, the key sections that require pre-set protection margins are determined. Extract the impedance aggregation state and power transmission trajectory of each interval point in the key section, locate the impedance jump region from the impedance aggregation state, construct an energy distribution pattern based on the impedance jump region, and phase-correlate the energy distribution pattern with the power transmission trajectory to form an impedance-power-energy coupling situation. Based on the impedance-power-energy coupling situation, activate the protection adjustment time window, establish the adjacent node communication protocol based on the protection adjustment time window, collect adjacent state information through the adjacent node communication protocol, and construct a cooperative distance protection potential field based on the adjacent state information; A gradient scan of the protection range is performed in the cooperative distance protection potential field. The gradient scan is performed along the normal direction of the equipotential surface of the potential field. The scan path starts from the local extreme point of the potential field and advances along the direction of maximum gradient, recording the boundary changes of the protection range. Based on the scan results, a cooperative network between nodes is constructed. Energy distribution analysis is performed on the cooperative network to determine the cooperative coverage area. Vector superposition is used to synthesize the coverage area, the key section, and the impedance-power-energy coupling situation in multiple dimensions to generate a multidimensional protection decision domain. A global search is performed on the multidimensional protection decision domain to locate the optimal balance point between protection reliability and selectivity from the search results. A hierarchical protection setting sequence is generated based on the balance point. A node coordination adjustment strategy is formulated based on the cooperative network. The hierarchical protection setting sequence is optimized through the adjustment strategy to output the optimal protection configuration. After executing the optimal protection configuration, the dynamic impedance status of the bay ring is monitored in real time. Based on the dynamic impedance status, the critical point of protection state transition is identified. Based on the critical point of protection state transition, the node collaborative adjustment strategy is triggered to complete the intelligent anti-misoperation protection.

2. The method according to claim 1, characterized in that, The step of constructing a false trip risk transmission matrix by structuring the fault propagation records includes: Pattern mining is performed on the fault propagation records to extract historical fault patterns; Identify impedance-sensitive nodes based on the historical failure modes; Determine the distance offset from the protection boundary from the impedance change sensitive node; A fault trip risk transmission matrix is ​​constructed based on the distance protection boundary offset.

3. The method according to claim 1, characterized in that, The construction of the energy distribution pattern based on the impedance jump region includes: Energy convergence and dissipation characteristics are extracted based on the impedance jump region; Spatial mapping of the energy aggregation and dispersion characteristics yields the energy distribution profile; Energy concentration nodes are identified based on the energy distribution profile; An energy distribution pattern is constructed using the energy concentration nodes.

4. The method according to claim 1, characterized in that, The step of establishing an impedance-power-energy coupling state by phase correlation between the energy distribution pattern and the power transmission trajectory includes: Extract the energy change time series from the energy distribution pattern; Based on the power transmission trajectory, the power fluctuation characteristics are analyzed to obtain the power fluctuation time series; The correlation degree is determined by comparing the energy change time series with the power fluctuation time series in phase. Based on the degree of correlation, an impedance-power-energy coupling situation is formed.

5. The method according to claim 1, characterized in that, The establishment of the adjacent node communication protocol based on the protection adjustment time window includes: The communication time slot is set based on the protection adjustment time window; Node communication priorities are allocated within the communication time slot; Information exchange rules are formulated based on the node communication priorities. A communication protocol between adjacent nodes is established based on the aforementioned information exchange rules.

6. The method according to claim 1, characterized in that, The construction of the cooperative distance protection potential field based on the adjacent state information includes: The neighboring state information is used to analyze the node protection capability and extract the protection capability distribution; Potential energy distribution is obtained by performing potential energy mapping on the protection capability distribution; The direction of the protective force field is determined based on the potential energy distribution. Construct a cooperative distance protection potential field based on the direction of the protection force field.

7. The method according to claim 1, characterized in that, The construction of the inter-node collaborative network based on the scan results includes: Weaknesses are identified in the gradient scan results to obtain the weak points in the protection coverage. Based on the weak points in the protection coverage, an initial cooperative relationship is established, and a cooperative network between basic nodes is constructed. The collaboration intensity is evaluated based on the operational performance of the basic node collaboration network to obtain collaboration performance indicators. The initial collaboration relationship is dynamically adjusted based on the collaboration effect index to form an adaptive inter-node collaboration network.

8. The method according to claim 1, characterized in that, The method of using vector superposition to synthesize the action domain, the key section, and the impedance-power-energy coupling situation into a multidimensional protection decision domain includes: A spatial vector is generated based on the collaborative coverage scope, a weight matrix is ​​generated based on the key segment, and a first-level vector synthesis is performed based on the spatial vector and the weight matrix to obtain a primary synthesis result. The initial synthesis results are verified for consistency, and the second-level synthesis process is initiated based on the verification success status. A time-dimensional tensor is constructed based on the impedance-power-energy coupling situation. A candidate synthesis result is obtained by superimposing the time-dimensional tensor with the verified primary synthesis result. Based on the candidate synthesis results, an integrity check is performed, and a multidimensional protection decision domain with hierarchical verification is generated based on the check results.

9. The method according to claim 1, characterized in that, The critical point for identifying the protection state transition based on the dynamic impedance state includes: A density gradient distribution is constructed using the dynamic impedance state; Search for locations of gradient abrupt changes in the density gradient distribution; The stability of the gradient mutation location is evaluated to determine the critical point for transition to the protected state.

10. A large-distance interval ring distance protection anti-misoperation tripping device, characterized in that, include: The fault handling module is used to collect fault propagation records of each circuit breaker node in the long-distance interval ring, and to perform structured processing on the fault propagation records to construct a false trip risk transmission matrix. The spectrum analysis module is used to trace the fault impact path using the fault trip risk transmission matrix, perform wavelet decomposition on the fault impact path to obtain abrupt spectrum features, and determine the key sections that require preset protection margins based on the abrupt spectrum features. The energy situation module is used to extract the impedance aggregation state and power transmission trajectory of each interval point in the key section, locate the impedance jump region from the impedance aggregation state, construct the energy distribution pattern based on the impedance jump region, and perform phase correlation between the energy distribution pattern and the power transmission trajectory to form an impedance-power-energy coupled situation. The potential field construction module is used to activate the protection adjustment time window based on the impedance-power-energy coupling situation, establish a neighboring node communication protocol based on the protection adjustment time window, collect neighboring state information through the neighboring node communication protocol, and construct a cooperative distance protection potential field based on the neighboring state information. The decision generation module is used to perform a gradient scan of the protection range in the cooperative distance protection potential field. The gradient scan is performed along the normal direction of the equipotential surface of the potential field. The scan path starts from the local extreme point of the potential field and advances along the direction of maximum gradient, recording the boundary changes of the protection range. Based on the scan results, a cooperative network between nodes is constructed. Energy distribution analysis is performed on the cooperative network to determine the cooperative coverage area. Vector superposition is used to synthesize the coverage area, the key section, and the impedance-power-energy coupling situation in multiple dimensions to generate a multidimensional protection decision domain. The configuration optimization module is used to perform a global search on the multidimensional protection decision domain, locate the optimal balance point between protection reliability and selectivity from the search results, generate a hierarchical protection setting sequence based on the balance point, formulate a node collaborative adjustment strategy based on the cooperative network, and optimize the hierarchical protection setting sequence through the adjustment strategy to output the optimal protection configuration. The dynamic adjustment module is used to monitor the dynamic impedance state of the bay loop in real time after executing the optimal protection configuration, identify the protection state transition critical point based on the dynamic impedance state, and trigger the node collaborative adjustment strategy based on the protection state transition critical point to complete the intelligent anti-misoperation protection.

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