A current transformer secondary circuit characteristic parameter monitoring method and system

Abstract

The application provides a current transformer secondary circuit characteristic parameter monitoring method and system, the method comprises the following steps: injecting at least two excitation signals with different frequencies into the secondary circuit through electromagnetic coupling; collecting return signals to obtain high-frequency excitation impedance; establishing a frequency characteristic model containing an eddy current effect term and an interlayer effect term based on the high-frequency excitation impedance, and mapping the high-frequency excitation impedance to an equivalent power frequency excitation impedance; obtaining a load impedance, and constructing an error analysis model based on the equivalent power frequency excitation impedance, the load impedance and a secondary current to obtain a ratio error and a phase error; generating a fault diagnosis characteristic parameter set based on the above parameters, performing fault type identification, and outputting the fault type of the secondary circuit. The technical scheme of the application realizes fault type identification of the current transformer secondary circuit under the condition of no power outage, and improves the online monitoring capability.

Description

Technical Field

[0001] This invention belongs to the field of power system automation technology, specifically relating to a method and system for monitoring characteristic parameters of the secondary circuit of a current transformer. Background Technology

[0002] In power distribution network metering and protection applications, the secondary circuit of a 10kV current transformer carries secondary current signals and provides input to power metering devices, relay protection devices, and monitoring terminals. The operating environment of the secondary circuit is subject to factors such as electromagnetic interference, temperature rise variations, contact aging, and vibration loosening. Furthermore, the connection status and load status of the secondary circuit change with maintenance commissioning / discharging, equipment replacement, and changes in operating conditions. Since secondary circuit faults can lead to metering deviations, protection maloperation, or failure to operate, there is a continuous need for on-site monitoring of the secondary circuit's operating status and fault identification.

[0003] In existing technologies, the status assessment and fault diagnosis of the secondary circuit of current transformers are usually carried out by manual periodic inspections, power outage tests or live inspections. The secondary current waveform is collected and the harmonics, distortion and other indicators are analyzed to determine whether there are problems such as open circuit, poor contact, partial short circuit, abnormal load, etc. in the secondary circuit.

[0004] The insufficient adaptability of existing technologies in engineering applications is mainly reflected in the following aspects: First, in terms of implementation efficiency, both traditional tests requiring power outages and complex operations and wiring, and live-line inspections limited by on-site conditions, suffer from long cycles and high costs, making it difficult to achieve routine and continuous monitoring of the secondary circuit status. Second, regarding diagnostic mechanisms, existing methods mostly rely on current thresholds, single electrical parameters, or empirical rules, making it difficult to distinguish the impact of different causes such as connection anomalies, load changes, and changes in the magnetic properties of the transformer itself on errors at the physical level. This results in poor interpretability of diagnostic results and a high likelihood of false alarms or missed alarms. Finally, in terms of the temporal dimension of status characterization, the secondary circuit status is significantly affected by time-varying factors such as load fluctuations, temperature rises, and contact resistance degradation. Sampling and judgment based on discrete moments are insufficient to characterize the continuous evolution of the status, thus lacking the ability to capture early latent degradation and intermittent faults. Therefore, there is an urgent need for a method for monitoring characteristic parameters of the secondary circuit of current transformers to improve online monitoring capabilities, error assessment capabilities, and fault identification accuracy under uninterrupted power conditions. Summary of the Invention

[0005] This invention provides a method for monitoring characteristic parameters of the secondary circuit of a current transformer. The scheme of this invention obtains the high-frequency excitation impedance of the secondary circuit through heterogeneous excitation response, maps the high-frequency impedance to the power frequency equivalent excitation impedance, and constructs an error analysis model by combining load impedance and secondary current to obtain ratio error and phase error. Based on these parameters, a fault diagnosis feature set is generated to identify the fault type, thereby solving the technical problems of difficulty in obtaining key impedance parameters, difficulty in online assessment of operating errors, and difficulty in timely and accurate identification of fault types in the secondary circuit of current transformers under uninterrupted power supply conditions.

[0006] A first aspect of the present invention provides a method for monitoring characteristic parameters of the secondary circuit of a current transformer, the method comprising: At least two different frequencies of excitation signals are injected into the secondary circuit of the current transformer through a high-frequency injection module with a first electromagnetic coupling coil. The return signal of the secondary circuit is detected by a return detection module with a second electromagnetic coupling coil. The high-frequency excitation impedance of the secondary circuit is obtained based on the excitation signal and the return signal. A frequency characteristic model of the excitation impedance is established based on the high-frequency excitation impedance. The frequency characteristic model includes core eddy current effect terms and interlayer effect terms. Based on the frequency characteristic model, the high-frequency excitation impedance is mapped to the power frequency equivalent excitation impedance. Obtain the load impedance of the secondary circuit and the secondary current of the secondary circuit. Construct an error analysis model based on the power frequency equivalent excitation impedance, the load impedance, and the secondary current. Calculate the ratio error and phase error based on the error analysis model. A set of fault diagnosis feature parameters is generated based on the power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error. Fault type identification is performed based on the set of fault diagnosis feature parameters, and the fault type of the secondary circuit is output.

[0007] By employing the above scheme, the present invention provides a method for monitoring characteristic parameters of a current transformer secondary circuit. This method injects at least two different frequency excitation signals into the secondary circuit of the current transformer via electromagnetic coupling and detects the return signals to obtain the high-frequency excitation impedance of the secondary circuit, achieving online acquisition of key impedance parameters under uninterrupted power supply conditions. Furthermore, by establishing a frequency characteristic model of the excitation impedance based on the high-frequency excitation impedance, including eddy current and interlayer effect terms, the high-frequency excitation impedance is mapped to the power frequency equivalent excitation impedance, achieving a unified representation between high-frequency measurable parameters and power frequency equivalent parameters. Further, by acquiring the load impedance and secondary current, an error analysis model is constructed based on the power frequency equivalent excitation impedance, load impedance, and secondary current to obtain the ratio error and phase error, enabling online evaluation of operating errors. Significantly, by generating a set of fault diagnosis characteristic parameters based on the power frequency equivalent excitation impedance, load impedance, secondary current, and the aforementioned errors, and performing fault type identification, online output of secondary circuit fault types is achieved, improving the secondary circuit status monitoring and fault identification capabilities.

[0008] In some embodiments of the present invention, injecting at least two different frequencies of excitation signals into the secondary circuit includes: performing multi-frequency injection into the secondary circuit sequentially based on a preset set of frequency points, synchronously detecting the return signals corresponding to each frequency point, generating a return response sequence, and determining the frequency response curve of the high-frequency excitation impedance based on the return response sequence.

[0009] In some embodiments of the present invention, obtaining the high-frequency excitation impedance of the secondary circuit based on the excitation signal and the return signal includes: performing amplitude and phase extraction on the excitation signal and the return signal to obtain the amplitude component and phase component at each frequency point; determining the impedance amplitude and impedance phase of the high-frequency excitation impedance based on the amplitude component and phase component, and generating a complex representation of the high-frequency excitation impedance.

[0010] In some embodiments of the present invention, establishing the frequency characteristic model of the excitation impedance includes: expressing the high-frequency excitation impedance as a function of complex permeability; introducing eddy current effect terms and interlayer effect terms into the function to form a frequency characteristic expression of complex permeability that includes loss components and equivalent permeability correction components.

[0011] In some embodiments of the present invention, mapping the high-frequency excitation impedance to the power frequency equivalent excitation impedance includes: performing a two-dimensional correction process on the high-frequency excitation impedance based on the frequency characteristic model to obtain the equivalent complex permeability under power frequency conditions; and determining the power frequency equivalent excitation impedance based on the equivalent complex permeability.

[0012] In some embodiments of the present invention, obtaining the load impedance of the secondary circuit includes: obtaining a secondary circuit voltage sampling signal through a non-contact energy harvesting module; obtaining a secondary circuit current sampling signal; and determining the load impedance based on the voltage sampling signal and the current sampling signal.

[0013] In some embodiments of the present invention, the construction of the error analysis model includes: establishing an error expression based on the equivalent circuit of the current transformer; and incorporating the power frequency equivalent excitation impedance, the load impedance, and the secondary current into the error expression as input quantities to obtain an error analysis model for outputting ratio error and phase error.

[0014] In some embodiments of the present invention, generating the fault diagnosis feature parameter set includes: taking the power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error as impedance error type feature parameters; performing waveform analysis processing on the secondary current to obtain waveform type feature parameters; and aggregating the impedance error type feature parameters and the waveform type feature parameters to form the fault diagnosis feature parameter set.

[0015] In some embodiments of the present invention, the fault type identification includes: performing hierarchical identification processing based on the fault diagnosis feature parameter set to obtain a candidate fault type set; filtering the candidate fault type set based on a preset criterion to output the fault type of the secondary circuit.

[0016] Compared with existing technologies, the advantages of this invention are as follows: by performing multi-frequency excitation injection and return response synchronous detection on the secondary circuit, generating a return response sequence and determining the frequency response curve of the high-frequency excitation impedance, continuous characterization of the high-frequency impedance characteristics of the secondary circuit is achieved; by performing amplitude and phase extraction on the excitation signal and return signal and generating a complex characterization of the high-frequency excitation impedance, unified extraction of impedance amplitude and phase information is achieved; by expressing the high-frequency excitation impedance as a complex permeability function, and introducing eddy current effect terms and interlayer effect terms into the function to form a complex permeability frequency characteristic expression, constraint modeling of the frequency variation mechanism of the excitation impedance is achieved; by performing two-dimensional correction processing on the high-frequency excitation impedance based on the frequency characteristic model to obtain the equivalent complex permeability under power frequency conditions, and determining the power frequency equivalent excitation impedance based on the equivalent complex permeability, mapping from high-frequency measurable parameters to power frequency equivalent parameters is achieved; furthermore, by acquiring the secondary circuit electrical... By sampling voltage and current signals and determining the load impedance, the online acquisition of load-side parameters required for the error analysis model is achieved. An error expression is established based on the equivalent circuit of the current transformer, and the power frequency equivalent excitation impedance, load impedance, and secondary current are introduced into the error expression as input quantities to obtain an error analysis model for outputting ratio error and phase error, thus achieving online evaluation of operating errors. Furthermore, by using the power frequency equivalent excitation impedance, load impedance, secondary current, ratio error, and phase error as impedance error characteristic parameters, waveform analysis is performed on the secondary current to obtain waveform characteristic parameters, which are then aggregated to form a fault diagnosis characteristic parameter set, achieving feature organization for fault identification. This significantly improves the ability to obtain a candidate fault type set by performing hierarchical identification processing based on the fault diagnosis characteristic parameter set and to filter and output fault types according to preset criteria, achieving online identification and output of fault types in the secondary circuit of the current transformer.

[0017] A second aspect of the present invention provides a system for monitoring characteristic parameters of the secondary circuit of a current transformer, comprising: The data acquisition module is used to inject at least two different frequencies of excitation signals into the secondary circuit of the current transformer through a high-frequency injection module with a first electromagnetic coupling coil, and to detect the return signal of the secondary circuit through a return detection module with a second electromagnetic coupling coil, and to obtain the high-frequency excitation impedance of the secondary circuit based on the excitation signal and the return signal. The modeling module is used to establish a frequency characteristic model of the excitation impedance based on the high-frequency excitation impedance. The frequency characteristic model includes core eddy current effect terms and interlayer effect terms. Based on the frequency characteristic model, the high-frequency excitation impedance is mapped to the power frequency equivalent excitation impedance. The error analysis module is used to obtain the load impedance of the secondary circuit, obtain the secondary current of the secondary circuit, construct an error analysis model based on the power frequency equivalent excitation impedance, the load impedance, and the secondary current, and calculate the ratio error and phase error based on the error analysis model. The fault diagnosis module is used to generate a set of fault diagnosis feature parameters based on the power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error, perform fault type identification based on the set of fault diagnosis feature parameters, and output the fault type of the secondary circuit.

[0018] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention will become apparent from and be readily apparent in the description and the accompanying drawings. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0020] In the attached diagram: Figure 1 This is a schematic diagram of the overall hardware architecture of the secondary circuit characteristic parameter monitoring device provided in an embodiment of the present invention.

[0021] Figure 2 A high-frequency injection circuit diagram in the secondary circuit characteristic parameter monitoring device provided in an embodiment of the present invention.

[0022] Figure 3 The high-frequency filtering sampling circuit in the secondary loop characteristic parameter monitoring device provided in the embodiment of the present invention.

[0023] Figure 4 The power frequency current filtering and sampling circuit in the secondary circuit characteristic parameter monitoring device provided in the embodiment of the present invention.

[0024] Figure 5 This is a modular software architecture diagram of the secondary circuit characteristic parameter monitoring device provided in an embodiment of the present invention.

[0025] Figure 6 The ADC data acquisition process provided in this embodiment of the invention.

[0026] Figure 7 The equivalent circuit of the current transformer provided in the embodiment of the present invention.

[0027] Figure 8This is a schematic diagram of the equivalent circuit of a 10kV current transformer and its secondary circuit provided in an embodiment of the present invention.

[0028] Figure 9 This is a modular schematic diagram of the equivalent circuit diagram of a 10kV current transformer and its secondary circuit provided in an embodiment of the present invention.

[0029] Figure 10 The equivalent principle diagram of impedance measurement provided in the embodiments of the present invention.

[0030] Figure 11 The equivalent principle diagram for rapid impedance measurement calculation provided in the embodiments of the present invention is shown.

[0031] Figure 12 The equivalent excitation impedance frequency characteristics of current transformers from different manufacturers are provided for embodiments of the present invention.

[0032] Figure 13 The current interaction between iron core laminations is provided in the embodiments of the present invention.

[0033] Figure 14 The eddy current field distribution under the control of interlayer current is provided in the embodiments of the present invention.

[0034] Figure 15 This is a schematic diagram of the parameters of a layered 2D model provided in an embodiment of the present invention.

[0035] Figure 16 This is a schematic diagram of the iron core lamination structure provided in an embodiment of the present invention.

[0036] Figure 17 The schematic diagram of the excitation response signal of the metering current transformer provided in the embodiment of the present invention.

[0037] Figure 18 Typical hysteresis loop diagram of soft magnetic materials provided in the embodiments of the present invention.

[0038] Figure 19 The μ-H diagram of soft magnetic materials provided in the embodiments of the present invention.

[0039] Figure 20 The BH diagram of the soft magnetic material provided in the embodiments of the present invention.

[0040] Figure 21 The BH characteristics of silicon steel at different frequencies are provided in the embodiments of the present invention.

[0041] Figure 22 The μ-H characteristics of silicon steel at different frequencies are provided in the embodiments of the present invention.

[0042] Figure 23 The BH characteristics of silicon steel at different frequencies provided in the embodiments of the present invention are shown.

[0043] Figure 24 The μ-H characteristics of silicon steel at different frequencies of permalloy are provided in the embodiments of the present invention.

[0044] Figure 25 The permeability variation of three types of iron cores provided in the embodiments of the present invention.

[0045] Figure 26 The heat treatment temperature-timing diagram is provided for an embodiment of the present invention.

[0046] Figure 27 This is a schematic diagram of a typical structure of a miniature current transformer provided in an embodiment of the present invention.

[0047] Figure 28 The magnetic flux density-magnetic field strength curve is provided for an embodiment of the present invention.

[0048] Figure 29 The relative permeability-magnetic field strength curve is provided for an embodiment of the present invention.

[0049] Figure 30 The curve showing the change in inductance with outer diameter is provided for an embodiment of the present invention.

[0050] Figure 31 The curve showing the change in inductance with the number of turns is provided for an embodiment of the present invention.

[0051] Figure 32 The core inductance variation provided in the embodiments of the present invention Figure 33 This is a schematic diagram of a secondary circuit characteristic parameter monitoring method provided in an embodiment of the present invention.

[0052] Figure 34 This is a schematic diagram of a secondary loop characteristic parameter monitoring system provided in an embodiment of the present invention. Detailed Implementation

[0053] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0054] The following detailed description is exemplary and intended to provide a further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0055] Example 1: An embodiment of the present invention provides a secondary loop characteristic parameter monitoring device, the specific implementation of which is as follows: A 10kV current transformer secondary circuit characteristic parameter monitoring device includes coil C1, coil C2, main control module, high frequency injection module, PWM pulse width modulation module, high frequency sampling and filtering noise reduction module, current sampling module, communication interface module, and power supply module.

[0056] (1) Hardware system The hardware system adopts a modular design approach, using an ARM Cortex-M4 microcontroller as its core to build a high-performance, highly reliable embedded monitoring platform. The main control chip is the HC32F460 series, with a maximum operating frequency of 200MHz and a floating-point unit (FPU) to meet the real-time computation requirements of complex algorithms. See the overall architecture diagram for reference. Figure 1 .in: Main control module: Utilizing the HC32F460JETA as the main control chip, it features 512KB Flash and 192KB RAM, integrating a 12-bit ADC, timers, communication interfaces, and other rich peripherals. It is responsible for core tasks such as system resource scheduling, data processing, communication management, and fault diagnosis. Peripheral configurations include a watchdog circuit, power management module, and RTC real-time clock to ensure stable system operation.

[0057] High-frequency injection module: This module receives control signals from the main control module and sends high-frequency signals to the secondary circuit of the current transformer. It transmits the high-frequency signal to the secondary circuit of the current transformer via electromagnetic induction, using coil C1 as the carrier. A high-frequency signal injection circuit based on an H-bridge is designed, with an adjustable output frequency range of 1kHz-100kHz. A coupling circuit injects the high-frequency test signal into the secondary side of the current transformer, achieving non-contact impedance measurement.

[0058] refer to Figure 2 In the diagram, solid capacitor C37 is used to reduce the impedance within the high-frequency injection circuit, which can effectively increase the power frequency saturation current threshold (≥6A) and prevent the power frequency current from flowing back under the condition of a short circuit in the secondary circuit, which would cause a surge in power consumption and heat generation in the online monitoring module. The selection of MOSFETs Q1, Q2, Q3, and Q4 also requires a small Rds to reduce the impedance within the circuit.

[0059] PWM (Pulse Width Modulation) module: Used to adjust the high-frequency signal sent by the high-frequency injection module. (Reference) Figure 2 Two sets of high-precision complementary PWM outputs (PWM1 & PWM2, PWM3 & PWM4) are generated using the MCU's advanced timer TMR6, with a frequency range of 1kHz-100kHz and a resolution of 0.1%. The PWM control enables precise adjustment of the injected signal amplitude and also drives the signal isolation circuit, ensuring electrical isolation between the test signal and the main circuit, meeting the 10kV withstand voltage requirement.

[0060] High-frequency sampling and filtering / noise reduction module: This module acquires the return signal from the secondary circuit of the current transformer and sends it to the main control module. It uses electromagnetic induction, with coil C2 as the carrier, to acquire the return signal from the secondary circuit of the current transformer. A 24-bit high-speed ADC chip, MCP3914, is used, with a sampling rate ≥1MSPS, to achieve high-precision acquisition of the injected signal response. The front-end design incorporates a multi-stage analog filtering circuit, including a second-order Butterworth low-pass filter and a notch filter, effectively suppressing power frequency interference and high-frequency noise. The digital side implements an FIR / IIR digital filtering algorithm, improving the signal-to-noise ratio to over 60dB. (High-frequency filtering and sampling circuit reference) Figure 3 .

[0061] Current sampling module: Used to acquire the injected high-frequency signal current and send it to the main control module. It features 6 high-precision current transformer interfaces, with a range of 0-5A and an accuracy class of 0.2S. A 24-bit Σ-Δ ADC is used for synchronous sampling, with a sampling rate of 48kHz and a dynamic range of 120dB. It supports real-time calculation of parameters such as current RMS value and harmonic content. Reference circuit for power frequency current filtering and sampling is provided. Figure 4 .

[0062] Communication interface module and main control module: Connects to and is used for communication between the main control module and the outside world. It integrates multiple interfaces including RS485, Ethernet, and 4G / 5G wireless communication. The RS485 uses an isolated transceiver and supports the Modbus-RTU protocol. The Ethernet supports the DL / T698.45 protocol. The wireless communication module supports the MQTT protocol to achieve reliable data transmission with the main station system.

[0063] Power Module: Provides operating power for each functional module. The device uses ±5V DC power and employs internal multi-stage power conversion to provide +5V, +3.3V, etc., power to each functional module. It is designed with EMC protection capabilities, with electrostatic discharge immunity of 4kV (contact) and 8kV (air); surge immunity of 4kV (differential mode) and 6kV (common mode). The enclosure protection rating is IP54, suitable for outdoor cabinet environments.

[0064] (2) Software system refer to Figure 5 The software system adopts a layered architecture design, which is divided into a hardware driver layer, an operating system layer, an application service layer and a human-computer interaction layer from bottom to top. It is embedded with the FreeRTOS real-time operating system, with a task scheduling cycle of ≤10ms. It includes an impedance measurement algorithm, an operation error calculation module, a hybrid fault diagnosis algorithm, a few-sample learning module, a data processing and communication module, an IAP upgrade module and other auxiliary function modules.

[0065] Memory allocation: Using an HC32F460 as the main control MCU, its internal SRAM is 192K, which is relatively tight. Therefore, a simple memory allocation is performed in advance for each task function.

[0066] Table 1: Memory Allocation

[0067] System interrupt prediction places functions that require frequent and rapid responses in the system into an internal high-speed execution area (SRAMH) to improve system efficiency.

[0068] Table 2: System Interruption Usage Estimates

[0069] Table 3: System Task Forecast

[0070] ADC Data Acquisition Function: In the ADC data acquisition task, the injected and returned data of phases A, B, and C share a single memory. The power frequency data of phases A, B, and C are stored separately in fixed memory. The ADC data uses a process reference. Figure 6 Specifically: First, after the system starts up, it performs initialization configuration on the data and I2S modules. This initialization configuration is used to set the sampling channels, data buffers, clock parameters, and interface operating modes to establish the basic operating environment required for subsequent data acquisition and transmission.

[0071] After completing the initialization of the data and I2S module, the DMA data acquisition interrupt is enabled. This DMA data acquisition interrupt switches the data transfer process from processor-driven reading to direct memory access, improving data transfer efficiency and reducing the processor's workload during continuous sampling.

[0072] After enabling the DMA data acquisition interrupt, the system enters the parallel acquisition phase. On one hand, it acquires three-phase injection return data; on the other hand, it acquires three-phase power frequency data. The three-phase injection return data is used to characterize the return response of each phase circuit under the action of the injection signal, and the three-phase power frequency data is used to characterize the operating status data of the current transformer secondary circuit under power frequency conditions.

[0073] After collecting the three-phase power frequency data, a sampling process is performed on the three-phase power frequency data. The sampling process is used to extract data points from the continuously collected power frequency data that meet the requirements of subsequent analysis, so as to reduce the amount of redundant data and improve the efficiency of subsequent processing.

[0074] After completing the three-phase injection return data acquisition, the three-phase power frequency data acquisition, and the power frequency data sampling processing, the system determines whether the current acquisition task is complete. If the determination result is incomplete, it returns to continue executing the three-phase injection return data acquisition and three-phase power frequency data acquisition; if the determination result is complete, it proceeds to the next processing stage.

[0075] Once the data acquisition is complete, the system transmits the acquired data and corresponding semaphores to the injection return task. The data supports subsequent analysis and processing of the injection return response, while the semaphores are used to synchronize the acquisition process with the injection return task and trigger execution.

[0076] Furthermore, in this embodiment, by first performing interface initialization, then enabling DMA data acquisition interrupt, and then acquiring three-phase injection return data and three-phase power frequency data in parallel, and then passing the data and semaphores to the injection return task after acquisition, the data connection between the acquisition link and the subsequent processing link is realized, which improves the real-time performance and task scheduling efficiency of the data acquisition process.

[0077] The algorithm used in this invention is as follows: 1) Impedance Measurement Algorithm: Based on frequency domain analysis, the amplitude and phase information of the injected and response signals are extracted through FFT transformation to calculate the total impedance of the secondary circuit and the impedance characteristics at each frequency point. The least squares method is used to fit the excitation impedance curve to identify the core saturation characteristics. The algorithm implements optimized matrix operations, with a single calculation time ≤100ms.

[0078] 2) Operational Error Calculation Module: Based on the GB1208-2016 "Current Transformer" standard, the module calculates the ratio difference and phase difference in real time. By comparing the converted primary current value with the measured secondary current, a weighted average algorithm is used to eliminate the influence of transient disturbances, with a designed calculation accuracy of 0.05%. An error temperature compensation model is established to correct the influence of ambient temperature on the measurement results.

[0079] 3) Hybrid Fault Diagnosis Algorithm Module: This module integrates the Levenberg-Marquardt (LM) optimization algorithm with a backpropagation (BP) neural network to construct a two-level diagnostic architecture. The LM algorithm is used for rapid convergence calculation of feature parameters to identify typical fault modes such as open circuits, inter-turn short circuits, and overloads. The BP neural network (3-layer structure, with 15 hidden neurons) is used for complex fault mode identification, and fault type expansion is achieved through few-shot learning techniques. The system is pre-loaded with 500 sets of typical fault samples, supports online learning and updates, and achieves a diagnostic accuracy of ≥95%.

[0080] 4) Few-shot learning module: To address the problem of insufficient rare fault samples, a few-shot learning algorithm based on transfer learning is designed. Data augmentation techniques are used to expand the sample space, and meta-learning strategies are combined to quickly adapt to new fault types. The module supports a combination of local model training and cloud-based collaborative optimization.

[0081] The data processing and communication process is as follows: 1) Multi-channel data acquisition: Simultaneous acquisition and buffering of six-channel multi-source data, including current, temperature, and humidity, using a circular buffer management mechanism with a storage depth of ≥10,000 points. A timestamp alignment algorithm is designed to ensure the timing consistency of multi-channel data, with an alignment accuracy of ≤1ms.

[0082] 2) Waveform Reconstruction and Spectrum Analysis: Implements frequency domain analysis based on FFT / DFT, with a frequency resolution of 0.1Hz, capable of identifying harmonic and interharmonic components in the 0-50kHz range. Develops algorithms for extracting over 20 feature parameters, including waveform distortion rate, harmonic content, and spectral envelope. Supports historical waveform playback and abnormal waveform trigger storage functions.

[0083] 3) Communication Protocol Implementation: Following DL / T698.45 "Electric Energy Information Acquisition and Management System Part 4-5: Energy Controller Communication Protocol", dedicated data items such as excitation impedance (data identifier 0x00XX01) and operating error parameters (data identifier 0x00XX02) are added. A complete protocol stack including APDU message encapsulation, link layer management, and session layer security authentication is implemented. Communication modes such as active reporting, passive response, and heartbeat maintenance are supported, and the data reporting cycle is configurable (default 15 minutes).

[0084] 4) Upward communication and algorithm chip communication mechanism: External communication adopts UART mode, and protocol stack matching is required according to the communication protocol. APP upgrade function: The main processor requests dynamic memory to store the bin file, and then determines the upgrade target. If the coprocessor is to be upgraded, the bin file is distributed in segments.

[0085] Other functions: LED task: system running indicator; RTC second task: system second task registration and time control; shell task: facilitates system debugging, testing, information printing, etc.; system monitoring task: timed dog feeding to monitor whether each task is running normally, etc.; Esam security certification: improves system security.

[0086] (3) Error analysis model of 10kV current transformer based on characteristic vector impedance To achieve characteristic parameter extraction and online monitoring of 10kV current transformers, an error analysis model based on characteristic vector impedance is proposed. The equivalent excitation impedance, load impedance, and secondary circuit current are used as characteristic quantities to calculate the error of the 10kV current transformer in operation. A non-contact, different-frequency excitation response technique for obtaining the transformer's excitation impedance is proposed, and a complex permeability frequency characteristic model of the 10kV current transformer's excitation impedance is constructed based on the analytic hierarchy process (AHP). Finally, a non-contact energy extraction technique for extracting the transformer's load impedance is also proposed.

[0087] The equivalent circuit of a current transformer, such as Figure 7 As shown, primary current Equal to the excitation branch current and load branch The sum of the excitation currents The root cause of error in current transformers is shown in Formula 1: .

[0088] According to the electromagnetic induction theorem and Ampere's circuital law, the ratio error and phase error of a 10kV current transformer can be further expressed as Equation 2:

[0089] Z 02 And α is the secondary load and its impedance angle. l , μ , s , ψ These are all core parameters. m Excitation impedance of 10kV current transformer + U2 and I2 are the secondary terminal voltage and secondary circuit current of the 10kV current transformer in operation, respectively.

[0090] Figure 8 In the 10kV current transformer model shown, since the excitation branch voltage and the load branch voltage are the same, the error of the 10kV current transformer can be expressed as: Formula 3:

[0091] Formula 4:

[0092] Based on Formula 4, the error of a 10kV current transformer can be calculated using the equivalent excitation impedance and load impedance of the transformer.

[0093] This invention proposes a method for measuring the excitation impedance of a current transformer based on non-contact heterogeneous frequency excitation response technology, thereby indirectly obtaining the high-frequency excitation impedance of the current transformer. The specific implementation method is as follows: Figure 9As shown, by means of electromagnetic induction, different frequency signals are injected into the secondary circuit of the current transformer using coil C1 as the carrier, and different frequency return signals in the secondary circuit are detected using coil C2 as the carrier, thereby obtaining the characteristic impedance of the current transformer.

[0094] The equivalent principle diagram of the characteristic impedance measurement of a current transformer based on the non-contact, different-frequency excitation response technology for extracting the excitation impedance of the current transformer is shown below. Figure 10 As shown: in and These are the equivalent excitation impedances of the high-frequency injection coil and the return coil, respectively. and These are the injected high-frequency current signal and the detected high-frequency voltage signal, respectively.

[0095] During the calculation, refer to Figure 11 As shown, where: .

[0096] therefore:

[0097] Therefore, the equivalent excitation impedance of the current transformer can be calculated as follows:

[0098] To convert the high-frequency excitation impedance of the current transformer obtained based on the non-contact heterodyne excitation response method into the power frequency impedance, it is necessary to establish a frequency characteristic model of the excitation impedance of a 10kV current transformer to achieve high- and low-frequency mapping of the excitation impedance. Therefore, this invention establishes a frequency characteristic model of the excitation impedance based on the analytic hierarchy process (AHP) and considering interlayer interactions.

[0099] To better establish an error analysis model for a 10kV current transformer, it is first necessary to obtain the impedance distribution of the 10kV current transformer at different frequencies. Equivalent excitation impedance tests and calculations were conducted for different transformer ratios, manufacturers, and frequencies. The results are as follows: Figure 12 As shown, the impedance distribution range of 10kV current transformers with different turns ratios at different frequencies is large and exhibits nonlinearity.

[0100] When an iron core is subjected to a time-varying electromagnetic field, eddy currents are generated in the laminations. These eddy currents gradually concentrate on the surface of the core as the excitation voltage frequency increases; this phenomenon is called the "eddy current effect." Due to the eddy current effect, the electrical parameters of the iron core change with frequency, exhibiting "frequency-dependent characteristics." Under power frequency conditions, the insulating coating on the laminations restricts the flow of eddy currents between the laminations; therefore, the interlayer resistance current is very small. The interlayer gaps between silicon steel sheets are extremely short, resulting in very small interlayer capacitance and consequently, negligible capacitive current. At higher frequencies, the capacitive component of the interlayer current increases, forming many small eddy current loops within the core. As the frequency further increases, the interlayer current becomes an eddy current surrounding the entire core, forming a larger eddy current loop. This phenomenon is called the "interlayer effect."

[0101] Figure 13 This is a schematic diagram illustrating the interaction of currents between laminations in the iron core. The expression for interlayer current will be provided later. The components of magnetic field strength and current density are increased in the dimension. Interlayer current flows along... Flow in the axial direction, such as Figure 13 As shown, The thickness of the laminate; Interlayer gap; Interlayer current density; The eddy current density; For along The peak value of the alternating magnetic field intensity applied in the direction; For the magnetic field strength at Components along the axial direction; For the magnetic field strength at The component along the axial direction.

[0102] Figure 14 This represents the eddy current field distribution dominated by interlayer current. As the frequency increases, the magnetic field inside the laminations gradually shifts from the center of the core towards the surface. Simultaneously, the current distribution will change in relation to the interlayer penetration depth. Relatedly, considering the effects of interlaminar and eddy currents, the interlaminar current and its generated magnetic field cannot be ignored when the frequency does not exceed a certain threshold. At higher frequencies, eddy currents flow across the entire surface of the iron core rather than the surface of a single lamination; that is, under high-frequency conditions, a thin cylinder can be used to equivalently replace the iron core. The existence of eddy current and interlaminar effects causes changes in the magnetic flux path, which determines the distribution of the leakage field. Therefore, it is necessary to consider the effects of eddy current and interlaminar effects when establishing the iron core model.

[0103] The amplitude range of the swept-frequency signal injected using the frequency response method is typically 100V-2kV. Generally, the voltage amplitude of the injected signal is one order of magnitude smaller than the operating voltage indicated on the transformer nameplate. Even under power frequency conditions, the induced magnetic field generated by these injected signal voltages is far lower than that of the core material B. The saturation "inflection point" of the H-curve indicates that the permeability of the core material is in the linear region. Under low-field conditions, the relative permeability... μ r Approximate to the initial permeability of the iron core μ i , μ i Defined as the relative permeability at zero magnetic field strength, i.e.:

[0104] For transformer core laminations, the thickness is significantly smaller than the width and length. At higher frequencies, the skin effect causes eddy currents to flow closer to the conductor surface. Therefore, a one-dimensional bath current model can be used to approximate the bath current in the transformer core laminations, where is the bath current density.

[0105] A magnetic field is applied in the Z direction, causing eddy currents to flow in the Y direction. According to Faraday's law of electromagnetic induction, neglecting interlayer current, the diffusion equation is written.

[0106]

[0107] In the formula: k is the propagation constant, and its calculation formula is:

[0108] In the formula: Represents electrical conductivity; Permeability of free space; The frequency of the alternating magnetic field; Where is the relative permittivity. The following is the formula for calculating the magnetic field strength on the surface of a Wield core:

[0109] In the formula: This represents the magnetic field strength at the surface of the iron core; the lamination width is... The cross-sectional area is The magnetic flux. Therefore, Spatial average magnetic flux density in the direction It is related to the magnetic flux through the cross-sectional area, that is:

[0110] The complex permeability of the iron core laminations is the sum of the spatial average magnetic flux density and the surface magnetic field strength. The ratio, that is:

[0111] To account for the effects of interlayer effects, a 2D model is adopted. Interlayer current in the direction of the model is used to correct the initial calculation formula for complex permeability based on the one-dimensional model.

[0112] Figure 15 This is a schematic diagram of the parameters of a 2D model. yes The magnetic field strength of the alternating magnetic field applied to the region. In the analysis, the following simplifications are made: 1) It is assumed that the magnetic material is linear and is isotropic with respect to small variations around any operating point. 2) Within the considered frequency range, the relative permeability and conductivity of the core material are independent of frequency.

[0113] The problem is divided into two parts, which are to be solved in two regions: the core lamination region (region 1); and the interlayer insulation region (region 2).

[0114] These two regions share the same mathematical description, which can be expressed according to the wave equation as follows:

[0115] In the formula: For materials The propagation constant, These represent the magnetic permeability, electrical conductivity, and relative permittivity of the corresponding materials, respectively. It is a material exist Magnetic field strength in the direction; For the iron core lamination area, This is the interlayer insulation region.

[0116] Assuming in and If the direction is symmetrical, then:

[0117] The boundary conditions are:

[0118] In the formula, Material exist The electric field strength in the direction.

[0119] One boundary condition is the normal ( The continuity of the magnetic field (direction), i.e. This means:

[0120] In the formula: It is a constant derived from the boundary conditions at the interface of the two materials; The coefficients are obtained through mathematical operations; .

[0121] Another boundary condition is tangential. direction The continuity of the electric field, that is ,but: (4.1-17) Algebraic calculations yield the following result:

[0122] In the formula: The equivalent relative complex permeability of the iron core; It is the stacking factor.

[0123] Relative complex permeability of iron cores including interlayer effects The magnetic field strength is obtained by measuring the average magnetic induction intensity in the laminated and interlayer regions, with the magnetic field strength at the region edge being... ,but:

[0124] When deriving the expression for the frequency-varying impedance of the iron core, it is assumed that for any silicon steel sheet stack, the electric field distribution and the magnetic field distribution are the same, and the electromagnetic field of the silicon steel sheet stack is regarded as a quasi-static field.

[0125] Figure 16 The schematic diagram of the iron core lamination structure is shown. To solve for the electric field strength, its differential equation is as follows:

[0126] The boundary conditions and initial conditions are as follows:

[0127] Solving the equations based on the above conditions, we obtain the electric field strength as follows:

[0128] Pick Based on the above formula, the expression for the core impedance can be obtained as follows:

[0129] In the formula: The magnetic permeability of the iron core; For the Laplace operator.

[0130] In summary, the error of an operating current transformer can be expressed by the following formula:

[0131] The schematic diagram of the transformer excitation impedance extraction method based on non-contact heterogeneous frequency excitation response technology is shown below. Figure 17 As shown. A high-frequency signal is first injected into the secondary circuit of the monitored metering current transformer, and then the returned signal is measured to calculate the high-frequency equivalent impedance. R0 is the sampling resistor of the injection front-end circuit, and R2 is the sampling resistor of the detection front-end circuit. The number of turns in coil T1 is n0, and the number of turns in coil T2 is n2. Z 1c This is the high-frequency equivalent impedance of coil T1. and For the injected high-frequency signal, A high-frequency signal is injected when the secondary circuit of the metering is open. This is the voltage across T1. and For the high-frequency signal injected into the metering secondary circuit, For coupling to the signal detection front-end current, Sampling resistor The voltage at both ends.

[0132] To extract impedance characteristics of current transformers using the high-frequency signal injection-return method and establish an error analysis model for a 10kV current transformer, it is necessary to obtain the magnetic performance parameters of the high-frequency iron core. Common parameters of power transformer core materials are shown in Table 4. Currently, the most commonly used is iron-based nanocrystalline alloy, which is an alloy composed mainly of iron with small amounts of Nb, Cu, Si, and B elements. It is formed into an amorphous material through a rapid solidification process. After heat treatment, microcrystals with a diameter of 10-20 nm are obtained, dispersed on the amorphous matrix, and are called microcrystalline or nanocrystalline materials. Compared with other commonly used core materials, nanocrystalline materials have excellent comprehensive magnetic properties, including high saturation magnetic induction, high initial permeability, low coercivity, low high-frequency loss under high magnetic induction, and higher resistivity than permalloy. There are currently three main types of nanocrystalline alloys, as shown in Table 5. Table 4: Performance parameters of common core materials

[0133] Table 5: Performance Comparison of Different Domestic Ultrafine Crystal Grades

[0134] For 10kV current transformer cores and high-frequency cores, the loop method is used to conduct magnetic performance testing. Primary and secondary coils are wound on the core. Current is injected into the primary terminal, and the induced voltage is measured at the secondary terminal. The BH curve is calculated from the UI curve of the secondary voltage and primary current using the following formula: ,

[0135] like Figure 18-20 As shown, the μ-H and BH curves were measured, and it can be seen that the saturation magnetic flux density is about 1.1T, the permeability before saturation is 34mH / m (relative permeability of 27,000), and the magnetic field strength at saturation is 30A / m.

[0136] The following description illustrates the high-frequency characteristics of different core materials. First, the high-frequency magnetic properties of commonly used core materials such as silicon steel and permalloy were tested, and the results are as follows.

[0137] Depend on Figure 21 and Figure 22 It can be seen that the saturation magnetic flux density of silicon steel at different frequencies is about 1.8T, and the peak relative permeability at 50Hz is about 43,000. As the frequency increases, the permeability decreases significantly. When the frequency rises to 500Hz, the maximum relative permeability has dropped to below 10,000. When the frequency rises to 2.5kHz, the relative permeability has dropped to less than 5,000.

[0138] Depend on Figure 23 and Figure 24 It is known that the saturation magnetic flux density of permalloy is less than 1.0T, and the peak relative permeability at 50Hz can reach about 270,000. Therefore, permalloy is a good material for making high-precision power frequency current transformers. However, the relative permeability of permalloy drops to about 45,000 at 1kHz and even drops to less than 20,000 at 5kHz.

[0139] The permeability of both silicon steel and permalloy decreases significantly with frequency. Compared to the permeability at 50Hz, the permeability at 1kHz decreases by more than 50%, and the inductance also changes significantly with frequency. This makes it unsuitable for transmitting weak signals across a wide frequency range of 1kHz to 30kHz. Furthermore, silicon steel has drawbacks such as high hardness, making it difficult to process miniature iron cores, and permalloy has a high unit price. Therefore, nanocrystalline materials are proposed as the high-frequency iron core material because nanocrystalline iron cores have better frequency characteristics, and the magnetic characteristic curve can be easily adjusted by changing the heat treatment parameters.

[0140] For the three test cores, the magnetization curves were obtained respectively as follows: Figure 25 .

[0141] The relative permeability of the above-mentioned microcrystalline iron cores can reach up to 200,000, but it varies greatly at different frequencies, resulting in poor frequency consistency. Furthermore, as the magnetic field strength increases, the permeability initially rises sharply and then falls, failing to maintain stability. For the above three batches of cores, when used as high-frequency iron cores, their inductance and anti-saturation capability cannot be simultaneously satisfied. Therefore, improvements need to be made to the strip processing technology to simultaneously enhance both permeability and anti-saturation capability.

[0142] Using nanocrystalline alloys as the raw material for magnetic cores, the composition ratio is based on Fe, Si, B, Cu, and Nb, with the addition of one or more of the following elements: Co, Zr, Hf, C, Ge, and Mn. Compared with conventional nanocrystalline materials, the saturation magnetic flux density is increased from 1.2T to over 1.4T, and the resulting miniature current transformer has stronger anti-saturation capability.

[0143] After the core master alloy with a determined composition ratio is melted at high temperature, it is rapidly cooled into a thin metal strip using a pressure spraying method. This strip is then processed to the designed dimensions and wound. A vacuum high-temperature heat treatment with transverse magnetization is then performed. The key to this patent lies in selecting appropriate temperature control timing and magnetic field strength to obtain a core with magnetic properties that meet the requirements. The magnetic field strength is controlled by adjusting the magnetizing current. A typical temperature control timing sequence is as follows: Figure 26 As shown.

[0144] After heat treatment, the magnetic core is fitted with a protective outer shell. Enamelled wire is then evenly wound around the shell as winding 1 and winding 2. Depending on the installation method and transformation ratio design, one of the windings may also use a through-core configuration. The overall structure of the miniature current transformer is as follows: Figure 27 As shown.

[0145] The magnetic properties of the miniature current transformer are as follows: Figure 28 and Figure 29 As shown, within the measurement frequency band, as the current increases, before the magnetic core reaches near saturation and the permeability begins to decrease significantly, the magnetic flux density exhibits a positive correlation with the magnetic field strength, demonstrating good linearity. This means the linear range is relatively large, allowing for a higher permeability during normal operation and stronger anti-saturation capability. Furthermore, the permeability decay is not significant with increasing frequency, indicating good frequency response consistency in this type of transformer. With other parameters remaining constant, the transmission error of the miniature transformer is negatively correlated with the permeability. Therefore, this nanocrystalline core miniature transformer maintains good transmission characteristics even at non-power frequency conditions because the permeability change due to frequency variations is not significant.

[0146] For core inductance, the single-turn calculation method is as follows:

[0147] Inductance calculation method for an N-turn coil:

[0148] μ r Where is the relative permeability, k is the lamination factor (taken as 0.78), D is the outer diameter of the core, d is the inner diameter of the core, and h is the height of the core.

[0149] Taking a certain type of current transformer as an example, the measured permeability value is substituted into the formula to calculate the single-turn inductance:

[0150] When the number of turns is 30, the inductance is:

[0151] The results are consistent with those obtained using an LCR meter. The electromagnetic and geometric parameters of the high-frequency iron core need to be comprehensively considered in relation to the performance of the peripheral hardware circuitry, while also being constrained by spatial dimensions and other conditions. With the inner diameter and height constant, the inductance of a single-turn coil changes with the outer diameter as follows: Figure 30 As shown. With the inner and outer diameters and height remaining constant, the coil inductance changes with the number of turns as follows. Figure 31 As shown.

[0152] The inductance frequency characteristics of the excitation response coil were tested at high and low temperatures, such as... Figure 32 As shown.

[0153] Accuracy Analysis of Online Calculation Method for Feature Error a. Analysis of the influence of primary impedance of 10kVCT The internal impedance measuring device obtains the equivalent excitation impedance from the secondary side of the 10kV CT through a different frequency excitation response method. In reality, it should be the parallel value of the primary impedance and the excitation impedance. The primary impedance needs to be compensated.

[0154] In actual operation, the equivalent primary impedance of a 10kV CT comes from the impedance of the distribution transformer and the electrical load, and can be obtained from the line voltage of the distribution area and the primary current of the transformer.

[0155]

[0156] Zm is the actual excitation impedance of the 10kVCT; Zm0 is the excitation impedance obtained from the online monitoring prototype; I2 is the actual secondary current of the 10kVCT; UN is the line voltage of the system power load; K is the transformation ratio of the 10kVCT.

[0157] The sum of the current transformer's excitation impedance and secondary circuit impedance was obtained by adjusting the test parameters and using a different frequency excitation response method, thus obtaining the circuit impedance matrix. High and low frequency impedance tests were conducted on current transformers and their secondary circuits from different manufacturers, with varying ratios and grades, at frequencies of 50Hz, 3kHz, 5kHz, and 7kHz. The test data are shown in Table 6.

[0158] Table 6: High and Low Frequency Impedance Mapping Excitation Equivalent Impedance Data

[0159] The actual excitation impedance of a 10kV current transformer under 50Hz conditions ranges from 4.01Ω to 26.81Ω (this value is mainly related to the core material, turns ratio, and size of the transformer). Calculations using high-low frequency mapping yield an excitation impedance ranging from 3.94Ω to 26.35Ω. Compared to the measured values, the minimum deviation is -0.07Ω, the maximum deviation is -0.63Ω, the absolute value of the minimum deviation rate is 1.47%, and the absolute value of the maximum deviation rate is 2.8%.

[0160] Figure 33 This is a flowchart illustrating a method for monitoring characteristic parameters of a current transformer secondary circuit according to an embodiment of the present invention.

[0161] Example 2, as Figure 33 As shown, the present invention provides a method for monitoring characteristic parameters of the secondary circuit of a current transformer. The method is applied to the above-mentioned device and includes the following steps: S1. Inject at least two different frequency excitation signals into the secondary circuit of the current transformer through the high-frequency injection module with the first electromagnetic coupling coil, and detect the return signal of the secondary circuit through the return detection module with the second electromagnetic coupling coil. Based on the excitation signal and the return signal, obtain the high-frequency excitation impedance of the secondary circuit. S2. Establish a frequency characteristic model of the excitation impedance based on the high-frequency excitation impedance. The frequency characteristic model includes core eddy current effect terms and interlayer effect terms. Based on the frequency characteristic model, the high-frequency excitation impedance is mapped to the power frequency equivalent excitation impedance. S3. Obtain the load impedance of the secondary circuit, obtain the secondary current of the secondary circuit, construct an error analysis model based on the power frequency equivalent excitation impedance, the load impedance, and the secondary current, and calculate the ratio error and phase error based on the error analysis model; S4. Generate a set of fault diagnosis feature parameters based on the power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error. Perform fault type identification based on the set of fault diagnosis feature parameters and output the fault type of the secondary circuit.

[0162] By employing the above scheme, the present invention provides a method for monitoring characteristic parameters of a current transformer secondary circuit. This method obtains the high-frequency excitation impedance of the secondary circuit by injecting at least two different frequency excitation signals into the secondary circuit of the current transformer via electromagnetic coupling and detecting the return signals, thus achieving online acquisition of key impedance parameters under uninterrupted power supply conditions. Furthermore, by establishing a frequency characteristic model of the excitation impedance based on the high-frequency excitation impedance, including eddy current and interlayer effect terms, the high-frequency excitation impedance is mapped to the power frequency equivalent excitation impedance, achieving a unified representation between high-frequency measurable parameters and power frequency equivalent parameters. Further, by acquiring the load impedance and secondary current, an error analysis model is constructed based on the power frequency equivalent excitation impedance, load impedance, and secondary current to obtain ratio error and phase error, enabling online evaluation of operating errors. Significantly, by generating a set of fault diagnosis characteristic parameters based on the power frequency equivalent excitation impedance, load impedance, secondary current, and the aforementioned errors, and performing fault type identification, online output of secondary circuit fault types is achieved, improving the secondary circuit status monitoring and fault identification capabilities.

[0163] In some embodiments of the present invention, injecting at least two different frequencies of excitation signals into the secondary circuit includes: performing multi-frequency injection into the secondary circuit sequentially based on a preset set of frequency points, synchronously detecting the return signals corresponding to each frequency point, generating a return response sequence, and determining the frequency response curve of the high-frequency excitation impedance based on the return response sequence.

[0164] In some embodiments of the present invention, obtaining the high-frequency excitation impedance of the secondary circuit based on the excitation signal and the return signal includes: performing amplitude and phase extraction on the excitation signal and the return signal to obtain the amplitude component and phase component at each frequency point; determining the impedance amplitude and impedance phase of the high-frequency excitation impedance based on the amplitude component and phase component, and generating a complex representation of the high-frequency excitation impedance.

[0165] Specifically, in this embodiment, a high-frequency injection module is used to receive control signals from the main control module and send high-frequency signals to the secondary circuit of the current transformer. The high-frequency injection module sends high-frequency signals to the secondary circuit of the current transformer through electromagnetic induction, using coil C1 as a carrier. The high-frequency injection module adopts a high-frequency signal injection circuit based on H-bridge, with an adjustable output frequency range of 1kHz-100kHz. The high-frequency test signal is injected into the secondary side of the current transformer through a coupling circuit. Solid-state capacitors are configured in the circuit to reduce the impedance within the loop, and low Rds MOSFETs are selected.

[0166] The high-frequency sampling and filtering noise reduction module is used to collect the return signal of the secondary circuit of the current transformer and send it to the main control module. The high-frequency sampling and filtering noise reduction module collects the return signal of the secondary circuit of the current transformer through electromagnetic induction, using coil C2 as a carrier. The high-frequency sampling and filtering noise reduction module adopts a 24-bit high-speed ADC chip to achieve high-precision acquisition of the response to the injected signal. The front end is designed with a multi-stage analog filtering circuit, including a second-order Butterworth low-pass filter and a notch filter, which effectively suppresses power frequency interference and high-frequency noise. The digital end implements the FIR / IIR digital filtering algorithm, improving the signal-to-noise ratio to over 60dB.

[0167] The current sampling module is used to collect the current of the injected high-frequency signal and send it to the main control module; the current sampling module is equipped with 6 high-precision current transformer interfaces and uses a 24-bit Σ-Δ ADC to achieve synchronous sampling.

[0168] The communication interface module is connected to the main control module and is used for communication and interaction between the main control module and the outside world. The PWM pulse width modulation module is used to adjust the high-frequency signal sent by the high-frequency injection module. The PWM pulse width modulation module uses the MCU advanced timer TMR6 to generate two sets of high-precision complementary PWM outputs with a frequency range of 1kHz-100kHz and a resolution of 0.1%. The PWM control is used to achieve precise adjustment of the injection signal amplitude. At the same time, it is used to drive the signal isolation circuit to ensure that the test signal is electrically isolated from the main circuit and meets the 10kV withstand voltage requirement.

[0169] This embodiment extracts the excitation impedance of the current transformer based on non-contact heterogeneous excitation response technology. The high-frequency injection module injects different frequency signals into the secondary circuit of the current transformer through the coil. The high-frequency sampling and filtering noise reduction module uses the coil to detect the return signal and obtain the characteristic impedance of the current transformer.

[0170] The equivalent excitation impedance of a current transformer is calculated using the following formula:

[0171] in and These are the equivalent excitation impedances of the high-frequency injection coil and the return coil, respectively. and These are the injected high-frequency current signal and the detected high-frequency voltage signal, respectively.

[0172] In some embodiments of the present invention, establishing the frequency characteristic model of the excitation impedance includes: expressing the high-frequency excitation impedance as a function of complex permeability; introducing eddy current effect terms and interlayer effect terms into the function to form a frequency characteristic expression of complex permeability that includes loss components and equivalent permeability correction components.

[0173] In this embodiment, based on the analytic hierarchy process (AHP) and considering interlayer effects, and combined with the measured impedance values ​​of different types and ratio transformers, a complex permeability frequency characteristic model of excitation impedance is established to realize high- and low-frequency mapping of excitation impedance, and to convert high-frequency excitation impedance into power frequency impedance.

[0174] In some embodiments of the present invention, mapping the high-frequency excitation impedance to the power frequency equivalent excitation impedance includes: performing a two-dimensional correction process on the high-frequency excitation impedance based on the frequency characteristic model to obtain the equivalent complex permeability under power frequency conditions; and determining the power frequency equivalent excitation impedance based on the equivalent complex permeability.

[0175] When establishing the frequency characteristic model of excitation impedance complex permeability, eddy current effect and interlayer effect are considered. A two-dimensional model is used to correct the complex permeability calculation formula. The equivalent relative complex permeability of the iron core satisfies:

[0176] In the formula: The equivalent relative complex permeability of the iron core; It is the stacking factor.

[0177] In some embodiments of the present invention, obtaining the load impedance of the secondary circuit includes: obtaining a secondary circuit voltage sampling signal through a non-contact energy harvesting module; obtaining a secondary circuit current sampling signal; and determining the load impedance based on the voltage sampling signal and the current sampling signal.

[0178] In this embodiment, the secondary circuit current, voltage and impedance data are collected by the hardware system, filtered and denoised, and then the core algorithm in the software system is used to process the data and extract feature parameters.

[0179] In some embodiments of the present invention, the construction of the error analysis model includes: establishing an error expression based on the equivalent circuit of the current transformer; and incorporating the power frequency equivalent excitation impedance, the load impedance, and the secondary current into the error expression as input quantities to obtain an error analysis model for outputting ratio error and phase error.

[0180]

[0181] Z 02 And α is the secondary load and its impedance angle. l, μ, s, ψ These are all core parameters. The excitation impedance is for a 10k current transformer.

[0182] In some embodiments of the present invention, generating the fault diagnosis feature parameter set includes: taking the power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error as impedance error type feature parameters; performing waveform analysis processing on the secondary current to obtain waveform type feature parameters; and aggregating the impedance error type feature parameters and the waveform type feature parameters to form the fault diagnosis feature parameter set.

[0183] Data processing of the fault diagnosis feature parameter set includes multi-channel data timing alignment, waveform reconstruction, spectrum analysis and feature parameter extraction. The feature parameters include excitation impedance, secondary circuit impedance, current RMS value, harmonic content, waveform distortion rate and spectrum envelope.

[0184] In some embodiments of the present invention, the fault type identification includes: performing hierarchical identification processing based on the fault diagnosis feature parameter set to obtain a candidate fault type set; filtering the candidate fault type set based on a preset criterion to output the fault type of the secondary circuit.

[0185] Fault diagnosis includes typical fault identification and complex fault identification. Typical fault identification is achieved by using the LM algorithm to quickly converge feature parameters, while complex fault identification is accomplished by using a BP neural network. The fault sample is expanded by combining a few-shot learning module to improve the ability to identify rare faults.

[0186] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention employs non-contact, multi-frequency excitation response technology. It injects a high-frequency signal into the secondary circuit of a current transformer via a coupling coil and detects the return signal. Characteristic parameter extraction can be completed without disconnecting the original circuit wiring or interrupting power. Compared to traditional offline verification methods, it avoids the impact of power outages on the reliability of the power distribution network. It also eliminates problems such as changes in circuit impedance characteristics and measurement result distortion caused by the connection of measuring elements in contact measurements. The operation is safe and highly compatible. It achieves non-contact, uninterrupted online monitoring, ensuring continuous power supply.

[0187] 2. This invention fully considers the eddy current effect and interlayer effect of the current transformer core. Based on the analytic hierarchy process (AHP), it establishes a frequency characteristic model of the excitation impedance complex permeability, achieving accurate mapping of high-frequency excitation impedance to power frequency impedance. Using equivalent excitation impedance, load impedance, and secondary circuit current as core characteristic quantities, an error analysis model is constructed. Through high-precision ratio error calculation and phase error calculation, the source of error (deterioration of core magnetic properties or abnormal secondary circuit impedance) can be accurately distinguished, providing a reliable basis for evaluating the accuracy of current transformer measurement. The high-precision error analysis model constructed in this invention improves measurement accuracy.

[0188] 3. This invention employs a hybrid fault diagnosis architecture that integrates the LM optimization algorithm and the BP neural network. The LM algorithm enables rapid identification of typical faults such as open circuits and inter-turn short circuits, while the BP neural network accurately judges complex faults such as local insulation aging. A small-sample learning module based on transfer learning expands the rare fault sample space through data augmentation technology, effectively solving the problems of insufficient samples and low diagnostic accuracy in traditional systems when facing rare faults. This achieves early warning and accurate fault location, reducing risks such as protection malfunctions and metering disputes. The integrated intelligent fault diagnosis algorithm of this invention enhances fault early warning capabilities.

[0189] 4. This invention employs an ARM architecture main control chip, integrating a high-precision ADC, multi-level filtering and noise reduction modules, and a robust EMC-protected casing. It can withstand harsh environments such as high temperature and humidity in outdoor enclosures and strong electromagnetic interference, ensuring continuous and trouble-free operation. On the software side, it utilizes an embedded real-time operating system, supporting multi-channel data synchronous acquisition and timing alignment, achieving high-speed and reliable data transmission while balancing real-time performance, security, and scalability. This invention ensures stable and reliable operation of the device through optimized hardware and software design.

[0190] Figure 34 This is a flowchart illustrating a current transformer secondary circuit characteristic parameter monitoring system according to an embodiment of the present invention.

[0191] Example 3, as Figure 34 As shown, the present invention also provides a current transformer secondary circuit characteristic parameter monitoring system, including: a data acquisition module S11, a modeling module S12, an error analysis module S13, and a fault diagnosis module S14.

[0192] The data acquisition module S11 is used to inject at least two different frequencies of excitation signals into the secondary circuit of the current transformer through the high-frequency injection module with the first electromagnetic coupling coil, and to detect the return signal of the secondary circuit through the return detection module with the second electromagnetic coupling coil, and to obtain the high-frequency excitation impedance of the secondary circuit based on the excitation signal and the return signal. Modeling module S12 is used to establish a frequency characteristic model of the excitation impedance based on the high-frequency excitation impedance. The frequency characteristic model includes core eddy current effect terms and interlayer effect terms. Based on the frequency characteristic model, the high-frequency excitation impedance is mapped to the power frequency equivalent excitation impedance. Error analysis module S13 is used to obtain the load impedance of the secondary circuit, obtain the secondary current of the secondary circuit, construct an error analysis model based on the power frequency equivalent excitation impedance, the load impedance, and the secondary current, and calculate the ratio error and phase error based on the error analysis model. The fault diagnosis module S14 is used to generate a set of fault diagnosis feature parameters based on the power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error, perform fault type identification based on the set of fault diagnosis feature parameters, and output the fault type of the secondary circuit.

[0193] In the description of this specification, the references to terms such as "an embodiment," "example," and "specific example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it.

Claims

1. A method for monitoring characteristic parameters of the secondary circuit of a current transformer, characterized in that, The method includes the following steps: At least two different frequencies of excitation signals are injected into the secondary circuit of the current transformer through a high-frequency injection module with a first electromagnetic coupling coil. The return signal of the secondary circuit is detected by a return detection module with a second electromagnetic coupling coil. The high-frequency excitation impedance of the secondary circuit is obtained based on the excitation signal and the return signal. A frequency characteristic model of the excitation impedance is established based on the high-frequency excitation impedance. The frequency characteristic model includes core eddy current effect terms and interlayer effect terms. Based on the frequency characteristic model, the high-frequency excitation impedance is mapped to the power frequency equivalent excitation impedance. Obtain the load impedance of the secondary circuit and the secondary current of the secondary circuit. Construct an error analysis model based on the power frequency equivalent excitation impedance, the load impedance, and the secondary current. Calculate the ratio error and phase error based on the error analysis model. A set of fault diagnosis feature parameters is generated based on the power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error. Fault type identification is performed based on the set of fault diagnosis feature parameters, and the fault type of the secondary circuit is output.

2. The method for monitoring characteristic parameters of the secondary circuit of a current transformer according to claim 1, characterized in that, The process of injecting at least two different frequency excitation signals into the secondary circuit includes: performing multi-frequency injection into the secondary circuit sequentially based on a preset set of frequency points, synchronously detecting the return signals corresponding to each frequency point, generating a return response sequence, and determining the frequency response curve of the high-frequency excitation impedance based on the return response sequence.

3. The method for monitoring characteristic parameters of the secondary circuit of a current transformer according to claim 1, characterized in that, Obtaining the high-frequency excitation impedance of the secondary circuit based on the excitation signal and the return signal includes: Amplitude and phase extraction is performed on the excitation signal and the return signal to obtain the amplitude component and phase component at each frequency point; The impedance amplitude and impedance phase of the high-frequency excitation impedance are determined based on the amplitude component and the phase component, and a complex representation of the high-frequency excitation impedance is generated.

4. The method for monitoring characteristic parameters of the secondary circuit of a current transformer according to claim 1, characterized in that, The frequency characteristic model of the excitation impedance includes: The high-frequency excitation impedance is expressed as a function of complex permeability; By introducing eddy current effect terms and interlayer effect terms into the aforementioned functional relationship, a complex permeability frequency characteristic expression is formed that includes loss components and equivalent permeability correction components.

5. The method for monitoring characteristic parameters of the secondary circuit of a current transformer according to claim 1, characterized in that, The step of mapping the high-frequency excitation impedance to the power frequency equivalent excitation impedance includes: Based on the frequency characteristic model, a two-dimensional correction process is performed on the high-frequency excitation impedance to obtain the equivalent complex permeability under power frequency conditions. The power frequency equivalent excitation impedance is determined based on the equivalent complex permeability.

6. The method for monitoring characteristic parameters of the secondary circuit of a current transformer according to claim 1, characterized in that, The process of obtaining the load impedance of the secondary circuit includes: The secondary circuit voltage sampling signal is obtained through a non-contact energy harvesting module; Acquire the secondary circuit current sampling signal; The load impedance is determined based on the voltage sampling signal and the current sampling signal.

7. The method for monitoring characteristic parameters of the secondary circuit of a current transformer according to claim 1, characterized in that, The constructed error analysis model includes: An error expression is established based on the equivalent circuit of the current transformer; By incorporating the power frequency equivalent excitation impedance, the load impedance, and the secondary current into the error expression as input quantities, an error analysis model for outputting ratio error and phase error is obtained.

8. The method for monitoring characteristic parameters of the secondary circuit of a current transformer according to claim 1, characterized in that, The set of generated fault diagnosis feature parameters includes: The power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error are used as impedance error characteristic parameters. Perform waveform analysis on the secondary current to obtain waveform-type feature parameters; The impedance error type feature parameters and the waveform type feature parameters are combined to form the fault diagnosis feature parameter set.

9. The method for monitoring characteristic parameters of the secondary circuit of a current transformer according to claim 1, characterized in that... The execution failure type identification includes: Based on the set of fault diagnosis feature parameters, hierarchical identification processing is performed to obtain a set of candidate fault types; The candidate fault type set is filtered based on preset criteria, and the fault type of the secondary circuit is output.

10. A system for monitoring characteristic parameters of the secondary circuit of a current transformer, comprising: The data acquisition module is used to inject at least two different frequencies of excitation signals into the secondary circuit of the current transformer through a high-frequency injection module with a first electromagnetic coupling coil, and to detect the return signal of the secondary circuit through a return detection module with a second electromagnetic coupling coil, and to obtain the high-frequency excitation impedance of the secondary circuit based on the excitation signal and the return signal. The modeling module is used to establish a frequency characteristic model of the excitation impedance based on the high-frequency excitation impedance. The frequency characteristic model includes core eddy current effect terms and interlayer effect terms. Based on the frequency characteristic model, the high-frequency excitation impedance is mapped to the power frequency equivalent excitation impedance. The error analysis module is used to obtain the load impedance of the secondary circuit, obtain the secondary current of the secondary circuit, construct an error analysis model based on the power frequency equivalent excitation impedance, the load impedance, and the secondary current, and calculate the ratio error and phase error based on the error analysis model. The fault diagnosis module is used to generate a set of fault diagnosis feature parameters based on the power frequency equivalent excitation impedance, the load impedance, the secondary current, the ratio error, and the phase error, perform fault type identification based on the set of fault diagnosis feature parameters, and output the fault type of the secondary circuit.

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