Low-voltage switch cabinet contact degradation early warning algorithm

Abstract

The invention discloses a low-voltage switch cabinet contact degradation early warning algorithm. The method comprises the following steps of: performing spatial decoupling on multi-point temperature measurement data by utilizing a thermal coupling transfer matrix subjected to online correction to obtain single-contact equivalent heat source power of a target contact; in response to a load step event of the running current, locking a time window containing transient processes before and after the step; constructing a heat balance state equation containing contact resistance, equivalent heat resistance and equivalent heat capacity in the window, introducing physical constraint conditions of the heat source power and the contact resistance, and solving the three physical parameters at the same time through a joint parameter identification algorithm; and based on the contact resistance obtained by identification, constructing a normalized degradation index and generating an early warning. According to the method, the load step is used as natural excitation, physical mechanical decoupling of contact resistance and heat dissipation environment parameters is achieved, the problems of multi-heat-source thermal field aliasing and electricity-heat aging mechanism confusion are solved, and the accuracy and robustness of early warning are improved.

Description

Technical Field

[0001] This invention belongs to the field of electrical equipment condition monitoring, and in particular to an early warning algorithm for contact deterioration in low-voltage switchgear. Background Technology

[0002] As the terminal distribution equipment in a power system, the operational reliability of low-voltage switchgear directly affects power supply quality and safety. Overheating faults caused by poor contact are one of the most common failure modes in switchgear. If not detected in the early stages of degradation, the non-linear increase in contact resistance will lead to insulation aging, arcing short circuits, and even fires. Therefore, achieving early and accurate warning of contact degradation is of great significance for ensuring the safe operation of the power system.

[0003] Currently, monitoring technologies for switchgear contact temperatures are relatively mature, mainly including wireless passive surface acoustic wave temperature measurement, fluorescent fiber optic temperature measurement, and infrared imaging temperature measurement. At the data processing level, existing technologies mostly employ fixed temperature threshold alarms, temperature rise trend analysis, or temperature rise-load correlation prediction methods based on historical data. Some studies attempt to introduce simplified thermal circuit models or neural network algorithms to assess equipment status by the difference between the monitored temperature and the ambient temperature, or to estimate heat generation using steady-state thermal balance relationships.

[0004] However, existing technologies still have significant shortcomings in early warning capabilities under complex operating conditions, due to their inability to address two deep-seated problems: the spatial overlap of multiple heat sources and the physical coupling of electro-thermal parameters. Specifically, in densely packed switchgear, the temperature rise of a single sensor is often the result of the superposition of heat sources from multiple surrounding contacts. Existing point-to-point monitoring struggles to accurately pinpoint the specific contact that is heating up in the early stages. More importantly, relying solely on steady-state temperature rise cannot distinguish whether the temperature increase is caused by increased contact resistance (electrical degradation) or by increased thermal resistance due to dust accumulation or poor ventilation (thermal degradation). These two distinct fault mechanisms exhibit highly similar behavior at steady-state temperatures. The lack of physical decoupling between these two types of parameters leads to existing warnings often being delayed or prone to false alarms due to environmental and load fluctuations. Summary of the Invention

[0005] The purpose of this invention is to provide an early warning algorithm for contact deterioration in low-voltage switchgear, in order to solve the aforementioned problems in the prior art.

[0006] Technical solution: An early warning algorithm for contact degradation in low-voltage switchgear, comprising:

[0007] Obtain the single-contact equivalent heat source power sequence of the target contact, and the target contact equivalent temperature sequence obtained by mapping using the online corrected thermal coupling transfer matrix;

[0008] In response to the detection of a load step event in the switchgear operating current, a time window covering the period before and after the load step event is determined.

[0009] Based on the single-contact equivalent heat source power sequence, target contact equivalent temperature sequence, and ambient temperature sequence within the time window, a thermal equilibrium state equation including contact resistance, equivalent thermal resistance, and equivalent heat capacity is constructed.

[0010] Physical constraints on the power sequence of the equivalent heat source of a single contact and the contact resistance of the contact are introduced into the thermal equilibrium equation of state. The contact resistance, equivalent thermal resistance and equivalent heat capacity of the contact are solved simultaneously by a joint parameter identification algorithm.

[0011] A contact deterioration warning signal is generated based on the obtained contact resistance.

[0012] Beneficial effects: This invention utilizes load step as a natural excitation to achieve physical decoupling between contact resistance and heat dissipation environment parameters, solving the problems of aliasing of thermal fields from multiple heat sources and confusion of electro-thermal aging mechanisms, and improving the accuracy and robustness of early warning. Attached Figure Description

[0013] Figure 1 This is a flowchart of an early warning method for contact deterioration in low-voltage switchgear, as described in this application.

[0014] Figure 2 This is a flowchart illustrating the steps involved in constructing a constrained matrix regression model in an embodiment of this application.

[0015] Figure 3 This is a flowchart illustrating the steps for solving the heat source inversion model in this application embodiment.

[0016] Figure 4 This is a hardware architecture and functional module deployment diagram of the early warning system for low-voltage switchgear contact deterioration in this embodiment of the application. Detailed Implementation

[0017] To solve these problems, combined with Figures 1 to 4 The present invention will be specifically described through the following embodiments.

[0018] Example 1 describes the process of an early warning method for contact deterioration in low-voltage switchgear. This example captures naturally occurring load steps during operation and uses a physical inversion algorithm to extract the actual contact state parameters from the temperature rise response, thereby achieving accurate early warning of contact deterioration.

[0019] Step 101: Obtain the single-contact equivalent heat source power sequence of the target contact, and the target contact equivalent temperature sequence obtained by mapping using the online corrected thermal coupling transfer matrix;

[0020] In this embodiment, the single-contact equivalent heat source power sequence refers to the time sequence of heating power, after decoupling, that only includes the Joule heating caused by the target contact's own current. Specifically, to address the thermal field coupling problem caused by the close arrangement of multiple contacts in a low-voltage switchgear, the system does not directly use the original temperature measurement data as the heat source input. Instead, it first uses a heat source inversion algorithm to separate the multi-point temperature measurement data into the independent heat source power of each contact. The single-contact equivalent heat source power is typically measured in watts.

[0021] The target contact equivalent temperature sequence refers to a virtual temperature time series that maps the spatially distributed sensor temperatures back to the target contact body location using an online-corrected thermal coupling transfer matrix. The online-corrected thermal coupling transfer matrix describes the transfer relationship between the heat sources of each contact and the temperature rise at each measuring point; its elements reflect the current ventilation and heat dissipation conditions and geometric characteristics. Through this matrix mapping, temperature deviations caused by differences in sensor placement can be eliminated, obtaining an equivalent temperature that more closely approximates the actual heating state of the contact.

[0022] As an optional implementation, for a low-voltage switchgear containing 6 contacts and 8 temperature measuring points, the system collects temperature data sequences from the 8 measuring points and current data sequences from the 6 circuits in real time. Using a pre-constructed or online-calibrated thermal coupling transfer matrix, the 8-dimensional temperature rise vector is converted into a 6-dimensional contact heat source vector, and the equivalent heat source power sequence of a single contact corresponding to the target contact is extracted. Simultaneously, using the forward transfer relationship or interpolation relationship of this matrix, the equivalent temperature sequence of the target contact surface is calculated.

[0023] Step 102: In response to the detection of a load step event in the switchgear operating current, determine the time window covering the period before and after the load step event;

[0024] In this embodiment, a load step event refers to a situation where the current in the switchgear circuit undergoes a sudden change in amplitude within a short period of time, and this continues for a certain period of time under high or low load conditions. For example, when a branch motor starts or a high-power load is connected, the current jumps from 30% to over 70% of the rated value and continues to operate for several minutes to tens of minutes. This natural load change provides the necessary excitation signal for physical parameter identification.

[0025] Determining the time window covering the period before and after a load step event specifically involves taking a segment of steady-state operating data ahead of the moment the load step occurs and a segment of data containing the complete transient temperature rise response afterwards, together forming a continuous time segment for parameter identification. The length of the time window is usually determined based on the thermal time constant of the switchgear to ensure coverage of the main process of temperature rise transitioning from one steady state to another.

[0026] Step 103: Based on the single-contact equivalent heat source power sequence, target contact equivalent temperature sequence and ambient temperature sequence within the time window, construct a thermal equilibrium state equation that includes contact resistance, equivalent thermal resistance and equivalent heat capacity.

[0027] In this embodiment, the system uses data captured within a time window to establish a physical model describing the thermal dynamics of the contact. The thermal equilibrium equation is a differential or difference equation based on the principle of energy conservation. Part of the heat generated by the contact heat source is used to raise the temperature of the contact and the surrounding medium, with the rate of temperature rise determined by the equivalent heat capacity; the other part is dissipated into the environment through conduction, convection, and radiation, with the steady-state temperature difference determined by the equivalent thermal resistance.

[0028] Contact resistance, equivalent thermal resistance, and equivalent heat capacity are the three key physical parameters to be identified in this equation. Contact resistance characterizes the degree of aging of the conductive contact surface, equivalent thermal resistance characterizes the smoothness of the heat dissipation path, and equivalent heat capacity characterizes the magnitude of thermal inertia. By constructing this equation, these three parameters are linked to the observable heat source power, contact temperature, and ambient temperature.

[0029] Step 104: Introduce physical constraints on the power sequence of the equivalent heat source of a single contact and the contact resistance of the contact in the thermal equilibrium equation of state, and solve the contact resistance, equivalent thermal resistance and equivalent heat capacity simultaneously through the joint parameter identification algorithm.

[0030] In this embodiment, to address the difficulty of distinguishing between an increase in heat source and a decrease in heat dissipation based solely on temperature response, this method introduces additional physical constraints during the identification process. Specifically, the physical constraint requires that the equivalent heat source power of a single contact should be approximately equal to the square of the current multiplied by the contact resistance. By incorporating prior knowledge from the field of electrical engineering into the thermal identification framework, the parameter solution space is narrowed.

[0031] Joint parameter identification algorithms estimate electrical parameters (contact resistance) and thermal parameters (equivalent thermal resistance and equivalent heat capacity) simultaneously within the same optimization objective or filtering framework. For example, a joint objective function is constructed that includes temperature fitting residuals and power constraint residuals, and an iterative algorithm is used to find the set of parameter solutions that minimizes the total residuals. This joint identification mechanism effectively decouples electrical and thermal degradation, accurately extracting the true value of the contact resistance.

[0032] Step 105: Generate a contact deterioration early warning signal based on the solved contact resistance;

[0033] In this embodiment, the system assesses the health status of the contacts based on the identified contact resistance value or its changing trend. Since contact resistance directly reflects the oxidation, corrosion, or loosening of the contact surface, early warning based on contact resistance is more sensitive and direct than early warning based solely on temperature. When the contact resistance exceeds a preset safety threshold or shows a significant increasing trend, the system generates a contact deterioration early warning signal, prompting maintenance personnel to conduct inspection or maintenance.

[0034] Example 2 describes how to perform online self-calibration of the thermal coupling transfer matrix using steady-state segments from operational data, and the specific process of obtaining high-quality single-contact heat source power based on a sparse inversion algorithm. This example provides an optimal implementation method for obtaining high-precision input data.

[0035] Step 201: Collect the cabinet current measurement sequence and cabinet temperature measurement sequence of the switchgear, and identify the steady-state time slice where the current fluctuation amplitude is lower than the preset threshold and the temperature change rate is lower than the preset slope; Step 202: Calculate the nominal heat source power sequence based on the pre-configured nominal contact resistance parameters and the cabinet current measurement sequence in the steady-state time slice.

[0036] Step 203: Based on the cabinet temperature measurement sequence, nominal heat source power sequence and preset initial topology within the steady-state time slice, construct a constrained matrix regression model and solve for the online corrected thermal coupling transfer matrix.

[0037] The thermal coupling matrix constraint fitting involves transforming the prior knowledge of the cabinet's thermal coupling topology into sparse structural constraints and smoothing regularization, as detailed below:

[0038] Sparse structure constraint: Based on the thermal coupling topology of the cabinet, determine which elements in the thermal coupling matrix H should be zero (corresponding to no significant thermal coupling path between the temperature measuring point and the contact). Define the set S of non-zero element locations, and express the constraint condition as follows:

[0039] H ij =0, if (i,j) does not belong to S;

[0040] This constraint reduces the number of free parameters in the thermal coupling matrix from m×n to |S|, thereby reducing the risk of overfitting and improving identification stability.

[0041] Laplace smoothing regularization: To constrain the smoothness of the thermal coupling matrix along the temperature measurement point dimension (physically, adjacent temperature measurement points should have similar responses to the same contact), a smoothing regularization term μ·‖L·H‖² is introduced. F The Laplace matrix L is a second-order difference matrix of (m-2)×m. For m temperature measurement points:

[0042] L=[1,-2,1,0,...,0;0,1,-2,1,...,0;...;0,...,0,1,-2,1];

[0043] The smoothing coefficient μ is selected to balance the fitting accuracy and the smoothness, with typical values ​​ranging from 0.01 to 0.1.

[0044] Non-negativity constraint: The thermal coupling coefficient should be physically non-negative (the heat source should only cause a temperature rise, not a temperature drop), therefore, constraint H... ij ≥0, Ψ(i,j)∈S; where Ψ means for any one.

[0045] The above three types of constraints are solved uniformly using the Alternating Direction Method of Multipliers (ADMM). The sparsity and non-negativity constraints are implemented through the projection step, and the smoothing regularization is incorporated into the quadratic objective function.

[0046] In this embodiment, the system defines the relevant data structures. Taking a typical low-voltage switchgear as an example, it is assumed that there are 6 contacts to be monitored and 8 temperature sensors. Cabinet current measurement sequence I meas Represented as a time series matrix containing 6 columns of current data, the cabinet temperature measurement sequence T sensor It is represented as a time series matrix containing 8 columns of temperature data.

[0047] A steady-state time slice refers to the period during which the switchgear is in a thermally stable state. The identification process is as follows: calculate the sliding variance or range of the current sequence. If it is less than a preset current fluctuation threshold (e.g., 5% of the rated current), and at the same time, the absolute value of the temperature change rate at all temperature measurement points is less than a preset slope threshold (e.g., 0.1 degrees Celsius per minute), then the time period is marked as a steady-state time slice.

[0048] During the steady-state time slice, the system utilizes the pre-configured nominal contact resistance parameter R. nom Calculate the nominal heat source power (e.g., factory value or standard value). The specific calculation formula is: Q nom_i =I i 2 *R nom_i Q nom_i Let I be the nominal heat source power of the i-th contact. i Let R be the current in the i-th contact. nom_i Let be the nominal contact resistance of the i-th contact.

[0049] The constrained matrix regression model aims to inversely deduce the thermal coupling transfer matrix H from steady-state data. For each steady-state time slice, the temperature rise vector Δ... T With the nominal heat source vector Q nom The linear relationship is approximately satisfied: △T ≈H*Q nom To ensure the physical validity of the solution, this embodiment constructs the following objective function: min||Δ T_window -H*Q nom_window || F 2 +μ*||L*H|| F 2 The first term is the data fitting term, representing the interpretability of matrix H for actual temperature rise data; the second term is the spatial smoothing regularization term, used to constrain the smoothness of the thermal coupling transfer matrix along the temperature measurement point dimension to prevent overfitting. L is the Laplace smoothing matrix, which can be specifically constructed as an operator performing second-order difference operations on the row vectors of matrix H.

[0050] Meanwhile, in order to conform to physical reality, the solution process must satisfy the following constraints:

[0051] 1. Nonnegativity constraint: H ij ≥0 indicates that the heat source can only cause the temperature to rise, but cannot cause the temperature to drop.

[0052] 2. Topological mask constraint: H ij =0 when Mask ij =0. The topology mask is a preset 0-1 matrix, determined according to the geometry of the switch cabinet. For contact and sensor pairs that are too far apart physically and have no direct heat transfer path, the corresponding elements are set to 0.

[0053] To address this optimization problem, this embodiment employs the Alternating Direction Multiplier Method (ADMM) for solution. The specific steps include: introducing an auxiliary variable Z, and alternately performing the unconstrained H update step (solving the linear equations with regularization terms) with the Z projection step (performing non-negative truncation and masking to zero) until convergence. By fusing the calculation results from multiple steady-state time slices, the online-corrected thermal coupling transfer matrix H is obtained. hat .

[0054] For example, the initial thermal coupling matrix H init It may be set according to the design drawings. After running for a period of time and capturing steady-state segments with multiple different load combinations, the H obtained is corrected using the above algorithm. hat It can accurately reflect the heat transfer characteristics under actual working conditions such as dust accumulation and fan aging.

[0055] Step 204: Construct a heat source inversion model with the online-corrected thermal coupling transfer matrix as the transfer operator and the real-time acquired cabinet temperature measurement sequence as the observation. Solve the heat source inversion model to obtain the single-contact equivalent heat source power sequence.

[0056] In this embodiment, the heat source inversion model aims to solve the inverse heat conduction problem, i.e., given the known results (temperature rise) and the transfer law (matrix H). hat The cause (heat source power Q) is then determined by inverse reasoning. For each time step t, the model is represented as: Δ T_meas (t)≈H hat *Q(t).

[0057] To overcome the ill-conditioned nature of the inverse problem and accurately separate multiple heat sources, this embodiment introduces sparsity constraints during the solution process. For each time step, the following optimization function is constructed: min||Δ T_meas (t)-H hat *Q(t)||2 2 +λ*||Q(t)||1 where the first term is the temperature rise reconstruction residual term, and the second term is the sparse regularization term (L1 norm). The sparse term is introduced because, in the early stages of degradation, typically only a few contacts exhibit abnormal heating, while most contacts generate relatively low or normal heat. Through L1 regularization, the algorithm tends to produce sparse solutions, enabling it to more accurately pinpoint the true sources of high heat and reduce false alarms and noise interference.

[0058] Under the non-negative power constraint (Q(t)≥0), the optimization function is solved using iterative algorithms, such as the coordinate descent method or the fast iterative shrinking threshold FISTA algorithm, to obtain the sparse heat source vector at that moment. The solution results of continuous time steps are concatenated in chronological order to obtain the equivalent heat source power sequence of a single contactor.

[0059] As a preferred implementation, the system also includes a quality control component. This is achieved using the inverted Q(t) and matrix H. hat Calculate the reconstructed temperature rise Δ T_recon (t)=H hat *Q(t), and calculate its residual e(t) = ||Δt||Q ... T_meas (t)-△ T_recon If the residual e(t) exceeds the preset confidence threshold, the inversion result at that time is marked as low confidence, and the weight of the data in that time period is reduced or it is removed in the subsequent parameter identification steps.

[0060] Step 205: Multiply the equivalent heat source power sequence of a single contact with the online-corrected thermal coupling transfer matrix to obtain the equivalent temperature sequence of the target contact. In other words, the heat source power is mapped to temperature rise based on the thermal coupling transfer matrix, and the equivalent temperature sequence is obtained by superimposing the ambient temperature.

[0061] In this embodiment, the target contact equivalent temperature sequence T contact (t) is calculated using the following formula: T contact_j (t)=T amb (t)+Σ(H hat_ji*Q i (t));

[0062] Or, more simply, using matrix H hat The heat source power is mapped back to the temperature space by corresponding to the main diagonal or main coupling element of the target contact itself. The temperature obtained in this way can isolate the influence of ambient temperature fluctuations and interference from other adjacent contacts, reflecting the temperature rise driving potential of the target contact itself.

[0063] The confidence assessment and time smoothing of the thermal coupling matrix and heat source sequence are performed as follows:

[0064] Reconstruction error and goodness-of-fit calculation: For each estimated time t, calculate the reconstruction temperature rise ΔT. recon (t)=H hat (t)·Q hat (t), and compared with the measured temperature rise ΔT meas (t) Comparison. Define the normalized root mean square error:

[0065] NRMSE(t) = sqrt(1 / m·Σe²) i (t)) / (max(ΔT meas )-min(ΔT meas ));

[0066] The goodness-of-fit coefficient γ(t) = 1 - NRMSE(t). When γ(t) > 0.9, the confidence level is high; when 0.7 < γ(t) ≤ 0.9, the confidence level is moderate; and when γ(t) ≤ 0.7, the confidence level is low and the value is marked as needing correction.

[0067] Consistency check between heat source estimation and current sequence: For each contact j, calculate the ratio sequence r. j (t)=Q hat_j (t) / I² j (t), this ratio should be approximately equal to the contact resistance value. Calculate its coefficient of variation CV. j =σ(r j ) / μ(r j If CV j A value >0.3 indicates that the heat source estimation is inconsistent with the current change, and the confidence weight of the corresponding estimation result needs to be reduced.

[0068] Confidence-based adaptive exponential smoothing: Time smoothing of the thermally coupled matrix using an exponentially weighted moving average.

[0069] H smooth (t)=α(t)·H raw (t)+(1-α(t))·H smooth (t-1);

[0070] Where the smoothing coefficient α(t) = αbase ·γ(t), α base Typical values ​​range from 0.1 to 0.3. New estimates are weighted more heavily when the confidence level is high, while historical values ​​are weighted more heavily when the confidence level is low.

[0071] A similar adaptive smoothing was applied to the heat source sequence, with the observation noise variance set to σ². v (t)=σ² v ,base / γ²(t) achieves the effect of relying more on predicted values ​​when the confidence level is low and trusting observed values ​​more when the confidence level is high.

[0072] Output Formation: After confidence assessment and time smoothing, a thermally coupled matrix estimate H is formed. hat and equivalent heat source sequence Q contact Together with the confidence level labels of each estimation result, these are used as inputs for subsequent thermal parameter identification and degradation assessment.

[0073] Example 3 describes the specific algorithm flow for simultaneously identifying contact resistance, equivalent thermal resistance, and equivalent thermal capacity using load step transient data.

[0074] Step 301: Calculate the time change rate of the switchgear operating current. When the time change rate exceeds the preset step judgment threshold and the high load duration after the step exceeds the preset thermal inertia time constant, it is identified as a load step event. Taking the occurrence time of the load step event as the center, extract a continuous time series containing the steady state segment before the step and the transient response segment after the step as the time window.

[0075] In this embodiment, the system continuously monitors the current sequence I(t). The rate of change over time, dI / dt, is calculated, for example, using the difference I(t) - I(t-Δt) / Δt. A preset step threshold can be set to 30% of the rated current per minute. Simultaneously, the system monitors the duration for which the current remains high (e.g., greater than 50% of the rated current) after a step. The preset thermal inertia time constant is typically determined based on the physical dimensions and material properties of the switchgear contacts, and is set, for example, to 15 to 30 minutes. Only when both the rate of change and the duration are met simultaneously is it considered a valid load step event, ensuring sufficient dynamic temperature change for identification.

[0076] The time window selection strategy is as follows: based on the step time t0, tw is taken forward by T. pre Data of duration (e.g., 10 minutes) is used as the initial steady-state baseline, and then truncation is performed for T. post Duration (e.g., 40 minutes) data is used as transient response data.

[0077] Step 302: The thermal equilibrium state equation is a discrete-time state-space model, and its mathematical form is: the value of the equivalent temperature sequence of the target contact at the next moment is equal to the value at the current moment plus the temperature change increment; wherein, the temperature change increment is directly proportional (positively correlated) with the equivalent heat source power sequence of a single contact, and inversely proportional to the temperature difference between the equivalent temperature sequence of the target contact and the ambient temperature sequence, and the proportionality coefficient is jointly determined by the contact resistance, equivalent thermal resistance and equivalent heat capacity of the contact.

[0078] In this embodiment, based on the first-order heat network model, the heat balance equation in continuous time is:

[0079] C θ *dT contact / dt=Q(t)–(T contact(t) –T amb(t) ) / R θ Discretize it (e.g., using the Euler method) to obtain a discrete-time state-space model:

[0080] T contact(k+1) =T contact(k) +(Delta t / C θ )*Q(k)-(T contact(k) -T amb(k) ) / R θ ;

[0081] Among them, T contact Q(k) is the target contact equivalent temperature at time k, Q(k) is the single contact equivalent heat source power at time k, and T is the effective heat source power at time k. amb (k) represents the ambient temperature, Δ t The sampling interval is given. This equation clearly characterizes the temperature evolution: the heat source Q(k) drives the temperature increase, and the temperature difference (T)... contact -T amb Heat is driven through thermal resistance R θ Dissipation, and heat capacity C θ It determines the rate of change (thermal inertia).

[0082] After constructing a single-contact thermal balance model, parameter decoupling is achieved by utilizing the physical constraint between contact resistance and heat source power. The specific mechanism is as follows:

[0083] The decoupling problem is proposed: In the single-contact lumped-parameter thermal equilibrium model, the steady-state temperature rise expression is ΔT. ∞ =Q·R θ =I²·R c ·R θ From the steady-state data, the contact resistance R c and thermal resistance R θ They are coupled in a product form and cannot be separated by temperature observation alone.

[0084] Based on the decoupling principle of transient response and physical constraints: This invention constructs the physical constraint Q=I²·R by introducing current measurement information. c Decoupling is achieved by combining transient response characteristics. The principle is as follows:

[0085] In the transient initial stage after a load step, the rate of temperature change dT / dt ≈ Q / C θ It is mainly determined by the power of the heat source and the heat capacity; the effect of thermal resistance has not yet been observed.

[0086] In the intermediate stage of the transient process, the time constant τ = R of the temperature curve. θ ·C θ The shape of the response can be determined from curve fitting.

[0087] During the approaching steady-state phase, the temperature rise ΔT ∞ =Q·R θ Provides thermal resistance information.

[0088] Since the heat source power Q can be obtained through inversion, and Q = I²·R c Since it is a deterministic physical relationship, R can be inferred from the identified Q and the measured I². c To achieve integration with R θ Decoupling.

[0089] Implementation of constraints: This invention employs a soft constraint method, which uses the prior estimate R of the contact resistance obtained from the heat source inversion. c ,prior=Q inverse / I² is introduced into the parameter identification process as a virtual observation. The noise variance of the virtual observation is determined based on the confidence level γ obtained from the heat source inversion. Q Dynamic adjustment:

[0090] σ² Rc =σ² Rc ,base / γ² Q ;

[0091] Where σ Rc ,base represents the baseline noise standard deviation, typically ranging from 10-20 μΩ. When the confidence level of the heat source inversion is high (γ Q >0.9), small noise variance, strong constraint effect, and identification results tend to converge with prior estimates; when confidence is low (γ Q <0.7), with large noise variance and weak constraint effect, identification mainly relies on temperature observation data.

[0092] Soft constraint mechanisms balance physical consistency and robustness to inversion errors, and are a key to achieving R... c With R θ The key to reliable decoupling.

[0093] Step 303 involves simultaneously solving for the contact resistance, equivalent thermal resistance, and equivalent heat capacity of the contact using a joint parameter identification algorithm. This includes: constructing a joint optimization objective function containing a temperature fitting residual term and a contact resistance physical constraint term; wherein the temperature fitting residual term represents the difference between the observed value of the target contact's equivalent temperature sequence and the model value predicted based on the thermal equilibrium equation of state; the contact resistance physical constraint term represents the difference between the single contact's equivalent heat source power sequence and the theoretical Joule heat calculated based on the current contact resistance estimate and operating current; and, under the premise of applying non-negative physical boundary constraints to the contact resistance, equivalent thermal resistance, and equivalent heat capacity, minimizing the joint optimization objective function using an extended Kalman filter algorithm, iteratively updating and outputting the contact resistance, equivalent thermal resistance, and equivalent heat capacity.

[0094] In this embodiment, the constructed joint optimization objective function J is as follows:

[0095] J=Σ[T meas(k) -T model(k) ] 2 +λ Rc *Σ[Q(k)-I(k) 2 *R c ] 2 ;

[0096] Among them, T meas (k) is the observed temperature (i.e., the equivalent temperature sequence of the target contact) obtained using the inversion algorithm, T model (k) is the model-predicted temperature recursively calculated based on the discrete state equations and current parameter estimates. The first term ensures that the thermal parameters conform to thermodynamic laws, and the second term utilizes electrical physical constraints (Q≈I). 2 R) strongly couples the heat source power with the contact resistance. λ Rc These are weighting coefficients used to balance the contributions of the two terms.

[0097] The solution process employs the Extended Kalman Filter (EKF) algorithm. The state vector X = [T] is defined. contact ], parameter vector θ=[R c ,R θ C θ In each iteration, the gradient is calculated using the Jacobian matrix to update the parameter estimates. Specifically, nonnegative physical boundary constraints are implemented during the update process. For example, after each parameter update, R is checked. c ,R θ C θ The value of the parameter is set. If a parameter is less than 0, it is forced to a small positive number (e.g., 1e-6) or kept at the value of the previous moment to prevent physically meaningless negative resistance or negative heat capacity.

[0098] Taking a specific numerical example, suppose contact 2 experiences a step temperature increase of 400A to 800A. If only the steady-state temperature rise is observed to be higher than normal, it is impossible to distinguish whether it is due to R... c Increase or R θ Increase. However, through the algorithm of this embodiment, utilizing the slope information and steady-state amplitude information of the step rise phase, combined with I... 2 With the R constraint, the algorithm can converge precisely to: R c The R value increased from 50 microohms to 55 microohms (contact degradation), while R θ Keep it unchanged at 0.5K / W (heat dissipation is normal).

[0099] Step 304, multi-window fusion and construction of contact thermal parameter time series, the specific process is as follows:

[0100] Organization of window-level estimation results: Assuming that N valid step windows are identified for contact j within the monitoring period, the identification result of each window is denoted as Θ. j,n ={R c,j,n ,R θ,j,n C θ,j,n ,t n ,γ n}, where t n γ is the center time of the nth window. n This is a reliability metric for the identification results of this window.

[0101] Credibility weight calculation: The credibility weight of each window is determined by a combination of the following factors:

[0102] Temperature variation amplitude factor w amp,n =min(1,(ΔT max,n -ΔT min,n ) / ΔT threshold ), where ΔT threshold =5℃ is the expected minimum temperature change range.

[0103] Fitting residual factor w res,n = exp(-RMSE² n / (2σ² res )), where RMSE n Let σ be the root mean square error of the temperature fitting for the nth window. res =1℃.

[0104] Parameter physical rationality factor w phys,n The identification parameter is determined based on whether it is within a reasonable range of 1, 0.5, or 0.1.

[0105] Overall weight w n =w amp,n ·w res,n ·w phys,n .

[0106] Time-weighted kernel smoothing fusion: For the thermal parameter estimation of contact j at time t, a time-weighted kernel smoothing method is used to fuse the multi-window results.

[0107] Θ hat_j (t)=Σ(w n ·K((tt n ) / h)·Θ j,n ) / Σ(w n ·K((tt n ) / h));

[0108] Where K(·) is the Gaussian kernel function K(u)=exp(-u² / 2), and h is the bandwidth parameter, typically taking a value that is 2-3 times the thermal inertia time constant.

[0109] Output: After multi-window fusion, a time series of contact thermal parameters Θ is generated. contact (t)=R c (t),R θ (t),C θ (t) is used for degradation assessment.

[0110] Example 4 describes how to construct a normalized degradation index based on the physical parameters identified by the front end, and how to achieve intelligent diagnosis of the contact state of the contact head through multi-scale trend analysis. This example solves the technical problems of traditional temperature rise monitoring being greatly affected by environmental and load interference and making it difficult to achieve horizontal comparison.

[0111] Step 401: Obtain the pre-stored reference contact resistance parameters of the target contact in a healthy state;

[0112] In this embodiment, the reference contact resistance parameter refers to the statistical value of the contact resistance measured or identified when the target contact is confirmed to be in a good contact state. The specific timing for obtaining this parameter is usually after the initial break-in phase when the switchgear is put into operation, or after a thorough overhaul and tightening maintenance.

[0113] Specifically, the system automatically extracts the identification result sequence within the aforementioned health status period (e.g., week 1 to week 4 after commissioning), removes outliers, calculates the arithmetic mean or median, and solidifies it as the reference contact resistance parameter for that contact. For example, for a circuit breaker contact with a rated current of 630 amps, the reference contact resistance parameter determined after initial operation monitoring might be 45 microohms.

[0114] In some alternative implementations, to ensure process consistency within the same batch of equipment, the reference contact resistance parameter can be the average value of a group of contacts of the same type and specification, or the factory test data provided by the manufacturer can be used directly.

[0115] Step 402: Calculate the ratio of the contact resistance of the contact to the reference contact resistance parameter, and construct the normalized degradation index;

[0116] In this embodiment, the purpose of constructing the normalized degradation index is to eliminate the inherent differences between different individual contacts and establish a unified health measurement standard. The normalized degradation index is a dimensionless numerical value that can intuitively reflect the rate of deterioration of the current contact state relative to the initial health state.

[0117] Calculating the ratio is the most direct way to construct this index. The specific formula can be expressed as: D t =R c_t / R base Among them, D t Let R be the normalized degradation exponent at time t. c_t R is the contact resistance of the contact obtained at time t. base This is the baseline contact resistance parameter. For example, if the currently identified contact resistance rises to 54 microohms, while the baseline value is 45 microohms, then the degradation index is 1.2.

[0118] As a preferred implementation, to improve the robustness of the index, the construction of the normalized degradation index can also incorporate other physical characteristics. For example, a weighted combination formula can be constructed: D t =w1×R c_ratio +w2×k ratio Among them, R c_ratio It is the ratio of contact resistance, k ratio This is the ratio of the normalized temperature rise index, where w1 and w2 are weighting coefficients. By introducing the temperature rise index as an auxiliary verification, misjudgments caused by errors in identifying a single parameter can be avoided.

[0119] Step 403: When the normalized degradation index exceeds the preset degradation threshold, a contact degradation warning signal is generated.

[0120] In this embodiment, the preset degradation threshold is a classification boundary determined according to power industry standards and the heat resistance level of the equipment insulation material. The degradation threshold is set with reference to the relevant power industry standards' requirements for limiting contact temperature rise. When the contact resistance increases to 1.2 times the reference value, the corresponding heat generation increases by approximately 44%, and the contact temperature rise begins to deviate from the normal level; when it increases to 1.5 times, the heat generation increases by 125%, which may lead to accelerated insulation aging; when it increases to 2.0 times, the heat generation increases by 300%, posing a risk of thermal runaway, which must be addressed immediately. The system typically sets multiple threshold levels. For example, when the degradation index exceeds 1.2, a concern-level warning signal is generated, indicating a significant increase in contact resistance; when the degradation index exceeds 1.5, a warning-level warning signal is generated, indicating a risk of localized overheating; when the degradation index exceeds 2.0, a danger-level alarm signal is generated, indicating that immediate shutdown and maintenance are necessary.

[0121] Step 404: Perform multi-scale trend analysis on the time series of the normalized degradation index;

[0122] In this embodiment, multi-scale trend analysis refers to examining the changing patterns of the degradation index simultaneously on both short and long time scales, aiming to distinguish between the slow natural aging of contact resistance and sudden accelerated degradation. Natural aging is usually a long process, which may span several years; while the vicious cycle caused by the rupture of the oxide film on the contact surface and the loosening of fasteners may develop rapidly in weeks or even days.

[0123] Step 405: Calculate the short-term sliding window slope and long-term moving average of the normalized degradation index;

[0124] In this embodiment, the slope of the short-term sliding window is used to capture the rapid deterioration trend of the contact state. Specifically, the system maintains a short sliding window (e.g., the results of the most recent 3 days or the most recent 5 step identifications), and uses the least squares method to perform linear fitting on the data points within the window. The slope of the fitted line is the slope of the short-term sliding window.

[0125] Long-term moving averages are used to characterize the baseline level of contact status. The system maintains a relatively long sliding window (e.g., the most recent 3 months or the most recent 100 identifications) and calculates the arithmetic mean of the data within the window. The slow rise of the long-term moving average reflects the natural wear and tear of the equipment's lifespan.

[0126] Step 406: If the slope of the short-term sliding window is greater than the preset acceleration factor, it is determined to be an accelerated deterioration state, and a contact deterioration warning signal containing the maintenance recommendation level is generated.

[0127] In this embodiment, the preset acceleration factor is a parameter characterizing the critical value of the degradation rate. When the short-term slope exceeds this factor, the contact resistance is increasing malignantly at a non-linear rate. Even if the absolute value has not yet reached the threshold of severe overheating, the system should immediately issue a warning.

[0128] Once a condition is determined to be in an accelerated deterioration state, the system generates a warning signal that includes not only alarm information but also a maintenance recommendation level. For example, if an extremely high short-term slope is detected, the system will output an instruction to immediately tighten or replace the contacts; if only the long-term average is slowly increasing, the system will output an instruction to check the contact surfaces during the next routine maintenance. This trend-based warning mechanism realizes the transformation from reactive alarms to predictive maintenance.

[0129] Example 5 describes the hardware architecture and functional module deployment for implementing an early warning algorithm for contact degradation in low-voltage switchgear. This example demonstrates the specific physical form of the technical solution.

[0130] The low-voltage switchgear contact deterioration early warning system provided in this embodiment mainly includes a sensor group, a data acquisition unit, an edge computing gateway, and a host computer management platform at the hardware level.

[0131] The sensor array is responsible for sensing raw physical quantities. Temperature acquisition typically uses passive wireless surface acoustic wave temperature sensors or infrared temperature probes, which are directly installed on the busbar or stationary contact seat near the contacts to ensure high-voltage isolation and temperature measurement safety. Current acquisition utilizes the current transformer built into the switchgear or a specially installed Hall current sensor to obtain the real-time load current of each circuit.

[0132] The data acquisition unit is responsible for the digitization and initial aggregation of analog signals, transmitting the data to the edge computing gateway via RS485 bus or wireless protocol. The edge computing gateway is the core processing unit of the system, equipped with a high-performance microprocessor that runs an embedded operating system and specific algorithm programs.

[0133] At the functional logic level, the system includes the following modules:

[0134] The data acquisition module is used to acquire the single-contact equivalent heat source power sequence of the target contact, and the target contact equivalent temperature sequence obtained by mapping using the online corrected thermal coupling transfer matrix;

[0135] In this embodiment, the module runs on an edge computing gateway or cloud server. It not only reads raw sensor data but also incorporates a heat source decoupling algorithm. Specifically, the module stores an online-updated heat coupling transfer matrix in its memory. Whenever new sampling data arrives, the module performs matrix operations and sparse inversion, outputting a decoupled heat source sequence and a corrected temperature sequence, providing clean input for subsequent processing.

[0136] The step response module is used to respond to the detection of a load step event in the switchgear operating current and to determine the time window covering the period before and after the load step event.

[0137] In this embodiment, the module scans the current data stream in real time. Internally, it maintains a circular buffer to cache historical data from a recent period. When the monitoring algorithm identifies a load step that meets the amplitude and duration requirements, the module triggers an event locking mechanism, automatically marking and extracting data segments containing the complete process before and after the step from the circular buffer, packaging them, and sending them to the parameter identification module.

[0138] The model building module is used to construct a thermal equilibrium equation of state, which includes contact resistance, equivalent thermal resistance and equivalent heat capacity, based on the single contact equivalent heat source power sequence, target contact equivalent temperature sequence and ambient temperature sequence within a time window.

[0139] In this embodiment, this module is responsible for filling discrete time-series data into a pre-defined physical model framework. It transforms continuous differential equations into discrete difference equations based on the sampling frequency and assembles them into a matrix or vector form to be solved. This module is also responsible for time alignment and outlier removal to ensure the quality of the data input to the model.

[0140] The parameter identification module is used to introduce physical constraints on the equivalent heat source power sequence and contact resistance of a single contact into the thermal equilibrium equation of state. The contact resistance, equivalent thermal resistance and equivalent heat capacity are solved simultaneously through the joint parameter identification algorithm.

[0141] In this embodiment, a nonlinear optimization solver (such as an extended Kalman filter library) is integrated. It receives the data structure output from the model building module and performs multiple iterative calculations until the parameters converge. This module ensures real-time computation through software locking or hardware accelerators, enabling it to output identification results within seconds of a single load step change.

[0142] The early warning generation module is used to generate early warning signals for contact deterioration based on the solved contact resistance.

[0143] In this embodiment, the module implements degradation assessment logic. It manages the equipment's health records and benchmark database, compares the real-time identified contact resistance with benchmark values, and performs trend analysis. Once an early warning condition is triggered, the module sends an alarm message to the operation and maintenance center via Ethernet or 4G / 5G network, or directly drives the on-site human-machine interface to issue an audible and visual alarm.

[0144] This embodiment uses a typical low-voltage switchgear as an example to describe in detail the complete implementation process of the method of the present invention. The switchgear includes 6 main circuit contacts and 8 wireless temperature measurement points, with a rated current of 1000A.

[0145] Cabinet structure and temperature measurement arrangement:

[0146] Contacts C1, C2, and C3 are located on the A, B, and C three-phase incoming side of the upper busbar, while contacts C4, C5, and C6 are located on the A, B, and C three-phase outgoing side of the lower busbar. Of the eight wireless temperature sensors, S1 is installed near C1, S2 is installed between C1 and C2, S3 is installed near C2, S4 is installed near C3, S5 is installed near C4, S6 is installed between C4 and C5, S7 is installed near C5, and S8 is installed near C6. Thermal coupling between the upper and lower busbars is negligible.

[0147] Initial thermal coupling matrix:

[0148] Based on the thermal coupling topology of the cabinet, an 8×6 dimensional initial thermal coupling matrix H is constructed. init (Unit: K / W):

[0149] H init =[0.45, 0.12, 0, 0, 0, 0; 0.25, 0.25, 0, 0, 0, 0; 0.10, 0.42,0.08, 0, 0, 0; 0, 0.10, 0.48, 0, 0, 0; 0, 0, 0, 0.46, 0.10, 0; 0, 0, 0, 0.22,0.22, 0; 0, 0, 0, 0.08, 0.44, 0.10; 0, 0, 0, 0, 0.12, 0.50]

[0150] Zero elements in the matrix indicate that there is no significant thermal coupling path between the corresponding temperature measurement point and the contact.

[0151] Steady-state time-slice thermal coupling matrix fitting:

[0152] During a certain operating cycle, a steady-state time slice w1 was identified, lasting 25 minutes, during which the three-phase current stabilized at I. A =650A、I B =680A、I C =640A, ambient temperature T amb =28℃.

[0153] Temperature rise vector ΔT at each temperature measurement point at the end of steady state w1 (Unit K) is: [14.3, 12.8, 16.1, 13.5, 15.8, 14.0, 17.2, 14.6] T .

[0154] Assume the initial contact resistance estimate R of each contact is... c, init=25μΩ, construct the nominal heat source vector Q nom, w1 (unit W):

[0155] Q nom C1 = 650² × 25 × 10⁻ 6 =10.56W; Q nom C² = 680² × 25 × 10⁻ 6 =11.56W; Q nom C3 = 640² × 25 × 10⁻ 6 =10.24W; Q nom C4 = 10.56 W; Q nom C5 = 11.56 W; Q nom C6 = 10.24 W.

[0156] The ADMM algorithm was applied, with a smoothing coefficient μ = 0.05. After 23 iterations, the solution converged, yielding the corrected thermal coupling matrix H. hat_w1 (Unit: K / W):

[0157] H hat_w1 =[0.48, 0.10, 0, 0, 0, 0; 0.28, 0.23, 0, 0, 0, 0; 0.08, 0.45,0.10, 0, 0, 0; 0, 0.12, 0.50, 0, 0, 0; 0, 0, 0, 0.50, 0.08, 0; 0, 0, 0, 0.25,0.24, 0; 0, 0, 0, 0.06, 0.48, 0.12; 0, 0, 0, 0, 0.10, 0.52];

[0158] Goodness of fit R² w1 =0.94.

[0159] Heat source inversion calculation:

[0160] The three-phase current is I at a certain operating moment A =720A、I B =750A、I C =700A, ambient temperature T amb =30℃, the temperature rise ΔT (in K) at each measuring point is: [18.2, 16.5, 21.8, 17.0, 19.5, 17.8, 22.5, 18.0] T .

[0161] By setting the regularization parameter λ=0.5, the FISTA algorithm is used to solve the sparse regularized inversion problem, and the equivalent heat source estimate Q is obtained. hat (Unit: W): [14.2, 16.8, 13.5, 15.0, 17.5, 13.8] T .

[0162] Calculate the equivalent contact resistance: R c C1 = 14.2 / 720² = 27.4 μΩ, R c C2 = 16.8 / 750² = 29.9 μΩ; R c C3 = 13.5 / 700² = 27.6 μΩ; R c C4 = 28.9 μΩ; R c C5 = 31.1 μΩ; R c C6 = 28.2 μΩ.

[0163] Verification: The root mean square of the residual between the reconstructed temperature rise and the measured temperature rise is 0.8K, which is less than the 1K threshold, indicating that the inversion result is reliable.

[0164] Load step transient identification:

[0165] For contact C2, a load step event was identified: the B-phase current jumped from 400A to 800A at 10:00. A data window was captured from 20 minutes before the step to 40 minutes after the step.

[0166] Key data within the window (partial sampling points):

[0167] Time (min) <![CDATA[I B (A)]]> <![CDATA[T amb (℃)]]> <![CDATA[T S3 (℃)]]> <![CDATA[Q C2 (W)]]> -20 400 27.5 32.1 4.0 0 400→800 27.6 32.2 4.0→16.0 +10 800 28.0 40.2 16.0 +40 800 28.4 51.2 16.0

[0168] Parameter identification is performed using an extended Kalman filter, with the initial state x C2 (0) = [32.0, 25.0, 0.45,450] T (Temperature, contact resistance μΩ, thermal resistance K / W, heat capacity J / K).

[0169] R obtained from heat source inversion c, prior=Q C2 / I² B As a virtual observation, the standard deviation of the virtual observation noise is set as σ based on the inversion confidence level. Rc =10μΩ.

[0170] After 61 iterations, the EKF algorithm converges, yielding: R c C2 = 24.8 μΩ; R θ C2 = 0.42 K / W; C θ C2 = 520 J / K.

[0171] Verification: The maximum error between the reconstructed temperature curve and the measured S3 temperature is 1.2℃, and the root mean square error is 0.6℃, indicating that the identification result is valid.

[0172] Degradation Index Calculation and Early Warning:

[0173] Contact C2 reference condition parameters: R c ,0=22μΩ,k0=0.0275K·A^ (-2) .

[0174] Current state parameter: R c =24.8μΩ, k=0.031K·A^2 (-2) .

[0175] Normalized degradation index: D C2 =0.6×(24.8 / 22)+0.4×(0.031 / 0.0275)=0.6×1.127+0.4×1.127=1.127.

[0176] Due to 1.0 <D C2 =1.127<1.2, contact C2 is in normal condition, no alarm is required, continue to monitor the trend of change.

[0177] Example 6: Describes the software product form of the technical solution.

[0178] This embodiment provides a computer-readable storage medium, such as a hard disk, flash memory, optical disk, or cloud virtual storage space. The storage medium stores a computer program or instruction code. When the computer program is loaded and executed by one or more processors (e.g., an embedded MCU, a digital signal processor (DSP), or a general-purpose central processing unit (CPU), all steps of the early warning method for contact degradation in low-voltage switchgear can be implemented.

[0179] Specifically, the computer program can be divided into multiple code segments, corresponding to functions such as data preprocessing, matrix operations, optimization solutions, and logical judgments. These code segments can be written in programming languages ​​such as C / C++, Python, or Matlab, and compiled and optimized to adapt to the target hardware platform's operating environment. By distributing and installing this storage medium in existing intelligent monitoring devices for switchgear, the software of existing equipment can be upgraded to possess the advanced physical early warning capabilities of this invention.

[0180] This invention, through the construction of a closed-loop diagnostic system based on physical inversion, achieves a leap from superficial monitoring to mechanism identification. Addressing the problem of spatial aliasing of multiple heat sources, the embodiments employ a matrix self-calibration and sparse inversion algorithm based on steady-state slicing. Without requiring additional hardware investment, it mathematically decomposes the superimposed temperature rise field observed by sensors into the equivalent heat source power of a single contact, effectively eliminating thermal interference from adjacent contacts and achieving precise spatial location of the fault source. Addressing the physical coupling problem of electrical and thermal parameters, the embodiments utilize naturally occurring load steps during power grid operation as excitation signals to capture the dynamic transient response of contact temperature rise. By constructing a state-space model including contact resistance (electrical parameter), equivalent thermal resistance, and thermal capacity (thermal parameter), and introducing current-power physical constraints for joint identification, the aging of the contact surface (R...) is successfully identified. c Increased) and deterioration of heat dissipation environment (R θ (Increased) to differentiate them. This allows maintenance personnel to accurately identify minute drifts in contact resistance in the early stages before the temperature exceeds the limit, eliminating the risk of misjudgment caused by fluctuations in ambient temperature and heat dissipation conditions.

Claims

1. An early warning algorithm for contact degradation in low-voltage switchgear, characterized in that, include: Obtain the single-contact equivalent heat source power sequence of the target contact, and the target contact equivalent temperature sequence obtained by mapping using the online corrected thermal coupling transfer matrix; In response to the detection of a load step event in the switchgear operating current, a time window covering the period before and after the load step event is determined. Based on the single-contact equivalent heat source power sequence, target contact equivalent temperature sequence, and ambient temperature sequence within the time window, a thermal equilibrium state equation including contact resistance, equivalent thermal resistance, and equivalent heat capacity is constructed. Physical constraints on the power sequence of the equivalent heat source of a single contact and the contact resistance of the contact are introduced into the thermal equilibrium equation of state. The contact resistance, equivalent thermal resistance and equivalent heat capacity of the contact are solved simultaneously by a joint parameter identification algorithm. A contact deterioration warning signal is generated based on the obtained contact resistance.

2. The method according to claim 1, characterized in that, The equivalent heat source power sequence of a single contact of the target contact, and the equivalent temperature sequence of the target contact obtained by mapping using the online-corrected thermal coupling transfer matrix, are achieved in the following way: Collect the cabinet current measurement sequence and cabinet temperature measurement sequence of the switch cabinet, and identify the steady-state time slices where the current fluctuation amplitude is lower than the preset threshold and the temperature change rate is lower than the preset slope; The nominal heat source power sequence is calculated based on the pre-configured nominal contact resistance parameters and the cabinet current measurement sequence within the steady-state time slice; Based on the cabinet temperature measurement sequence, nominal heat source power sequence and preset initial topology within the steady-state time slice, a constrained matrix regression model is constructed, and the online corrected thermal coupling transfer matrix is ​​obtained by solving it. A heat source inversion model is constructed using an online-corrected thermal coupling transfer matrix as the transfer operator and a real-time acquired cabinet temperature measurement sequence as the observation. The equivalent heat source power sequence of a single contact is obtained by solving the heat source inversion model. The equivalent heat source power sequence of a single contact is multiplied by the online-corrected thermal coupling transfer matrix to obtain the equivalent temperature sequence of the target contact.

3. The method according to claim 2, characterized in that, Constructing a constrained matrix regression model includes: Construct an objective function that includes a data fitting term and a spatial smoothing regularization term, where the spatial smoothing regularization term is used to constrain the second-order difference smoothness of the thermal coupling transfer matrix in the dimension of the temperature measurement point; Set non-negative constraints for matrix elements and topological mask constraints based on physical location relationships, where the topological mask constraints limit matrix elements without thermal coupling to zero; The objective function is minimized by the alternating direction multiplier method or the projected gradient method, and the thermally coupled transfer matrix is ​​obtained after online correction.

4. The method according to claim 2, characterized in that, Solving the heat source inversion model includes: For each time step, an optimization function is constructed that includes a temperature rise reconstruction residual term and a sparse regularization term; The optimization function is iteratively solved under non-negative power constraints to obtain the sparse heat source vector at this time step; By aggregating sparse heat source vectors in chronological order, a sequence of equivalent heat source power for a single contact is obtained.

5. The method according to claim 1, characterized in that, In response to the detection of a load step event in the switchgear operating current, the time window covering the period before and after the load step event is determined in the following way: Calculate the time change rate of the switchgear operating current. When the time change rate exceeds the preset step judgment threshold and the high load duration after the step exceeds the preset thermal inertia time constant, it is identified as a load step event. Centered on the moment of the load step event, a continuous time series containing the steady-state segment before the step and the transient response segment after the step is extracted as a time window.

6. The method according to claim 1, characterized in that, The thermal equilibrium equation of state is a discrete-time state-space model, and its mathematical form is represented as follows: The value of the equivalent temperature sequence of the target contact at the next moment is equal to the value at the current moment plus the temperature change increment. Among them, the temperature change increment is directly proportional to the equivalent heat source power sequence of a single contact and inversely proportional to the temperature difference between the equivalent temperature sequence of the target contact and the ambient temperature sequence. The proportionality coefficient is determined by the contact resistance, equivalent thermal resistance and equivalent heat capacity of the contact.

7. The method according to claim 1, characterized in that, The contact resistance, equivalent thermal resistance, and equivalent heat capacity of the contact are simultaneously solved using a joint parameter identification algorithm, including: Construct a joint optimization objective function that includes temperature fitting residual terms and contact resistance physical constraint terms; Among them, the temperature fitting residual term characterizes the difference between the observed value of the equivalent temperature sequence of the target contact and the model value predicted based on the thermal equilibrium equation of state; The physical constraint term for contact resistance characterizes the difference between the equivalent heat source power sequence of a single contact and the theoretical Joule heat calculated based on the current contact resistance estimate and operating current. Under the premise of applying non-negative physical boundary constraints to the contact resistance, equivalent thermal resistance, and equivalent thermal capacity of the contact, the extended Kalman filter algorithm is used to minimize the joint optimization objective function, and the contact resistance, equivalent thermal resistance, and equivalent thermal capacity of the contact are iteratively updated and output.

8. The method according to claim 1, characterized in that, Based on the solved contact resistance, a contact degradation early warning signal is generated, including: Obtain the pre-stored reference contact resistance parameters of the target contact in a healthy state; Calculate the ratio of the contact resistance of the contact to the reference contact resistance parameter, and construct a normalized degradation index; When the normalized degradation index exceeds the preset degradation threshold, a contact degradation warning signal is generated.

9. The method according to claim 8, characterized in that, The generation of contact deterioration early warning signals also includes: Multiscale trend analysis was performed on the time series of the normalized degradation index. Calculate the short-term sliding window slope and long-term moving average of the normalized degradation index; If the slope of the short-term sliding window is greater than the preset acceleration factor, it is determined to be an accelerated deterioration state, and a contact deterioration warning signal containing the maintenance recommendation level is generated.

10. An early warning system for contact deterioration in low-voltage switchgear, characterized in that, include: The data acquisition module is used to acquire the single-contact equivalent heat source power sequence of the target contact, and the target contact equivalent temperature sequence obtained by mapping using the online corrected thermal coupling transfer matrix; The step response module is used to respond to the detection of a load step event in the switchgear operating current and to determine the time window covering the period before and after the load step event. The model building module is used to construct a thermal equilibrium equation of state, which includes contact resistance, equivalent thermal resistance and equivalent heat capacity, based on the single contact equivalent heat source power sequence, target contact equivalent temperature sequence and ambient temperature sequence within a time window. The parameter identification module is used to introduce physical constraints on the equivalent heat source power sequence and contact resistance of a single contact into the thermal equilibrium equation of state. The contact resistance, equivalent thermal resistance and equivalent heat capacity are solved simultaneously through the joint parameter identification algorithm. The early warning generation module is used to generate early warning signals for contact deterioration based on the solved contact resistance.

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