Power system insulation resistance dynamic monitoring and self-adaptive balancing device based on GD32

Abstract

The invention discloses an intelligent insulation parameter dynamic monitoring and regulation system based on a GD32 microcontroller, and belongs to the field of power electronic insulation monitoring. According to the system, a distributed monitoring network is constructed by taking GD32 as a core processor, a multi-channel time-sharing sampling and dynamic impedance measurement algorithm is integrated, GA / GB dual-channel independent sampling is realized by virtue of a state machine, and the sampling efficiency is improved by 80% compared with that of a traditional scheme. A programmable resistance network and a low-noise operational amplifier are adopted, 1 pF to 100 nF impedance matching and 5-200-time dynamic gain adjustment are achieved, an optical coupler switching dynamic impedance measurement method is originally created, four sets of voltage are adopted for time-sharing conduction of a solid-state relay, and 1.5% high precision is achieved through an improved voltage division model. LC-pi type filtering is adopted at a signal input end, and the high-frequency noise suppression ratio is larger than 20 dB; and a CAN module is newly added, so that a CAN2.0B extension frame and an adjustable Baud rate of 10k-1Mbps are supported. Actual measurement shows that the system can measure 10-65M omega insulation resistance in new energy and rail transit scenes, the dynamic response is smaller than or equal to 200ms, the system is suitable for the fields of medical equipment, industrial sensors and the like, and the overall performance is improved by three times compared with that of a traditional scheme.

Description

Technical Field

[0001] This invention relates to the field of power system insulation monitoring technology, and is particularly applicable to real-time monitoring systems for the insulation status of power equipment under complex operating conditions. Specifically, it is an adaptive balancing method for insulation parameters based on dynamic impedance adjustment, used to achieve accurate monitoring and automatic compensation of insulation impedance. Background Technology

[0002] Currently, with the rapid development of smart grids, the demand for real-time monitoring of power system operation is increasing, especially in important power facilities such as substations, new energy power plants, and urban rail transit, where insulation monitoring technology plays a crucial role. However, power system insulation parameters are affected by various factors such as ambient temperature and humidity, equipment aging, and transient overvoltages. These factors can lead to fluctuations in insulation impedance and a decline in insulation performance, seriously affecting the safe and stable operation of the power system. Traditional insulation monitoring methods typically employ fixed threshold alarm mechanisms, which cannot be dynamically adjusted according to the actual operating status of the equipment, making it difficult to simultaneously ensure monitoring accuracy and response speed under complex operating conditions. Therefore, there is an urgent need for an intelligently adjustable insulation monitoring device to achieve accurate impedance measurement and adaptive balance. Existing insulation impedance detection technologies have the following drawbacks:

[0003] A single measurement requires 3-5 minutes of manual balancing, making multi-channel synchronous detection impossible;

[0004] It is only applicable to steady-state measurement with a response time >10s, and cannot capture transient insulation degradation, resulting in low accuracy.

[0005] Mechanical switches and relays have low switching sensitivity and are prone to mechanical wear, resulting in low reliability.

[0006] High-voltage testing requires manual operation and carries a risk of electric shock.

[0007] The lack of historical data comparison function makes it impossible to achieve trend prediction and fault warning. Summary of the Invention

[0008] The purpose of this invention is to provide a dynamic monitoring and adaptive balancing device for power system insulation impedance based on GD32, which can realize impedance measurement in a wide range of 10kΩ to 65MΩ (accuracy ±5%), automatically match voltage level requirements from 0.4kV to 11kV, support CAN data transmission, and improve measurement speed and accuracy by many times.

[0009] To achieve the above objectives, the technical solution of the present invention is as follows:

[0010] A power system insulation impedance dynamic monitoring and adaptive balancing device based on GD32 includes the following steps:

[0011] Step 1: The minimum system circuit of GD32F103CBT6 includes a basic crystal oscillator circuit; a hardware reset circuit active low; and a main flash boot mode configuration circuit.

[0012] Step 2: In the relay drive circuit, the main function of diode D1 (1N4148) is to protect the relay and transistor Q1 (SS8050) from reverse electromotive force. When the relay coil is de-energized, a reverse electromotive force is generated, which may damage the components in the circuit. Diode D1, also known as a freewheeling diode, ensures that the energy in the coil can be safely released, preventing high voltage generated when the coil current is suddenly interrupted from damaging the circuit.

[0013] Step 3, Intelligent resistor selection module: Dynamically switches the standard resistor network through an optocoupler-isolated solid-state relay array to form a programmable voltage division ratio; Signal conditioning unit: Contains a two-stage operational amplifier circuit, wherein: the front stage is configured as a voltage follower with an input impedance >10GΩ for impedance matching; the rear stage forms an inverting proportional amplifier with an adjustable gain range of 0.1-100 times; A 200Ω resistor is connected in series at the output of the operational amplifier to limit the ADC input current.

[0014] Step 4: Control the switching action of solid-state relays U4 and U5 using the GD32 microcontroller, and establish a voltage calculation model based on the voltage divider principle: Where Vp and Vn are the 12-bit ADC sampling values ​​of the microcontroller, and V is the excitation source voltage to be determined. Simultaneously, Kirchhoff's current law is used to establish the equation for the measurement loop: Where Rp and Rn are the insulation resistance values ​​of the positive and negative poles to be determined.

[0015] Step 5: Control the solid-state relays U4 to close and U5 to open using a microcontroller. Establish a measurement circuit based on Kirchhoff's current law. Use the black-box principle to derive a circuit containing only the measured resistors Rp and Rn and the sampling voltage Vp. / Vn / Simplified equation:

[0016] Step 6: Control the solid-state relays U4 to open and U5 to close using the microcontroller. Establish the measurement circuit based on Kirchhoff's current law, and derive the circuit using the black-box principle, which only includes the measured resistors Rp and Rn and the sampling voltage Vp. / / Vn / / Simplified equation:

[0017] Step 7: By simultaneously solving the upper and lower half-bridge measurement equations, establish a system of two linear equations in two variables, and solve for the precise values ​​of the positive-to-ground insulation resistance Rp and the negative-to-ground insulation resistance Rn.

[0018] Step 8: The CAN communication module uses a B0505S isolated power supply to achieve signal isolation. When a measurement command is received, it controls the relay K1 to close and start the insulation detection process, and collects the positive / negative voltage to ground and insulation resistance values ​​in real time. If the positive insulation resistance is detected to be lower than the threshold Rp or the negative insulation resistance is lower than the threshold Rn, an alarm message is immediately sent to the host computer through the CAN bus. The message format includes the fault polarity identifier, the measured resistance value and timestamp information.

[0019] In step 9, the power supply circuit converts the 24V DC power supply to +12V and -12V DC power supplies to provide dual power to the amplifier. The transformers (L6 and L7) included in the circuit not only provide electrical isolation but also effectively protect the circuit and the user from the potential effects of high voltage. Furthermore, the fuse design ensures timely power disconnection in case of overcurrent or overvoltage, thus protecting the entire circuit system. To further reduce ripple and noise in the power supply, a filter circuit composed of inductors and capacitors is used to ensure the stability of the output voltage. The use of the RS-2412 isolated power supply module achieves electrical isolation between 24V and 12V, thereby enhancing the safety and reliability of the circuit.

[0020] The method shown in step 7 specifically includes the following steps:

[0021] Step 7.1, from formula (1) in step 5 and formula (2) in step 6, we get

[0022] Step 7.2, let the formulas (3) and (4) from step 7.1 be used to... Then formulas (3) and (4) in step 7.1 can be rewritten as follows:

[0023] Step 7.3, from formulas (7) and (8) in step 7.2, we get

[0024] Step 7.4, substituting equation (10) into equation (9), we get... Compared with the prior art, the present invention has the following obvious advantages:

[0025] This invention simplifies the circuit structure for insulation resistance detection. Based on the principle of constant voltage division ratio, a system of two linear equations containing only Rp and Rn is established. Measurement is performed using the built-in 12-bit ADC of the GD32 microcontroller, and the line voltage drop is automatically compensated by a software algorithm, completely eliminating the dependence on the excitation source voltage measurement of traditional methods.

[0026] A multi-step state machine control algorithm is adopted, and four-stage voltage sampling is achieved through optocoupler switching. Combined with an improved voltage divider calculation model, the insulation resistance measurement accuracy reaches ±5%, which is 3 times higher than that of the traditional bridge method.

[0027] The dynamic impedance adjustment module adopts a high-speed solid-state relay array with a response time of ≤20ms and a compensation range covering 10kΩ~100MΩ. It can automatically match the voltage level requirements of 0.4kV~11kV.

[0028] In terms of anti-interference design, the system uses an LC-π type filter structure at the signal input end to form a cutoff frequency f. c The low-pass filter, combined with hardware-level mean filtering, achieves a high-frequency noise suppression ratio of >20dB.

[0029] A high-reliability CAN communication module is constructed using a B0505S isolated power supply, supporting CAN 2.0B extended frame format communication and an adjustable baud rate of 10kbps to 1Mbps. This module enables remote online configuration of parameters such as sampling period and alarm threshold. Attached Figure Description

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] Figure 1 A flowchart of the overall process of the power system insulation impedance dynamic monitoring and adaptive balancing device based on GD32 provided in this embodiment of the invention;

[0032] Figure 2 A simplified circuit diagram of a power system insulation impedance dynamic monitoring and adaptive balancing device based on GD32 provided in an embodiment of the present invention;

[0033] Figure 3 The main control principle diagram of the power system insulation impedance dynamic monitoring and adaptive balancing device based on GD32 provided in the embodiments of the present invention;

[0034] Figure 4 A schematic diagram of the insulation resistance detection principle of a power system insulation impedance dynamic monitoring and adaptive balancing device based on GD32 provided in this embodiment of the invention;

[0035] Figure 5 A communication principle diagram of a power system insulation impedance dynamic monitoring and adaptive balancing device based on GD32 provided in an embodiment of the present invention;

[0036] Figure 6 A power supply schematic diagram of a power system insulation impedance dynamic monitoring and adaptive balancing device based on GD32 provided for an embodiment of the present invention;

[0037] Figure 7 The host computer algorithm for a power system insulation impedance dynamic monitoring and adaptive balancing device based on GD32 provided in this embodiment of the invention;

[0038] Figure 8 The host computer of the power system insulation resistance dynamic monitoring and adaptive balancing device based on GD32, provided in the embodiments of the present invention, detects the value of insulation resistance and the voltage of excitation source. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this invention can be combined with other embodiments.

[0041] like Figure 1 The system flowchart shown clearly illustrates the complete workflow of the GD32 microcontroller from initialization and configuration, time-division sampling control to data processing and output, including key steps such as peripheral initialization, four-stage voltage sampling, digital filtering calculation and CAN communication. The error of each state switching time is controlled within 10ms, and the complete measurement cycle does not exceed 200ms.

[0042] like Figure 2The simplified circuit diagram shown reduces the actual measurement system to a purely resistive equivalent model, where the insulation resistances of the positive and negative terminals to ground are equivalent to Rp and Rn, respectively, the standard measurement resistance is equivalent to R, and the system voltage is equivalent to V. This simplified model clearly demonstrates the circuit topology changes of the four-step measurement method: first, V is measured with SW2 and SW3 closed and open; then, equations are established when SW2 is closed and SW3 is open, and again when SW2 is open and SW3 is closed; finally, the equations are solved simultaneously. This equivalent model allows for an intuitive understanding of the current path and key node voltage relationships under various measurement conditions. Verification shows that within the 10kΩ-65MΩ range, the calculated results deviate from traditional methods by less than 5%, effectively simplifying the understanding of the measurement principle.

[0043] like Figure 3 The diagram shown is the complete schematic of the GD32F103CBT6 minimum system, which includes the main control chip and its peripheral circuits, an 8MHz crystal oscillator clock circuit, a low-level active reset circuit, an SWD debugging interface, and a 3.3V power management circuit. BOOT0 is grounded through a 10kΩ resistor and configured for Flash boot mode. All power supply pins are equipped with 0.1μF decoupling capacitors and the layout conforms to high-frequency design specifications.

[0044] like Figure 4 As shown Figure 2 The engineering implementation circuit of the theoretical model safely adapts the high-voltage signal to the microcontroller's acquisition range through three-stage signal conditioning: the first stage uses a high-voltage divider network to attenuate the 1000Vmax input; the second stage uses a voltage follower (gain = 5±0.05%) composed of an SGM8273 precision operational amplifier, and integrates an RC filter; the third stage is set at the low end to build a precision inverter using an SGM8273 precision operational amplifier, and finally outputs a 0-3.3V standard signal to the ADC input pin of the GD32.

[0045] like Figure 5 The circuit shown is a high-performance CAN communication interface circuit. It uses a B0505S isolated power supply module to provide electrical isolation. The bus end is equipped with terminating resistors and filtering components to ensure signal integrity. It works with a CAN transceiver to achieve communication in accordance with the ISO11898-2 standard.

[0046] like Figure 6 The circuit shown is a power supply isolation circuit with a 24V input. A DC-DC isolation module converts the 24V input to a ±12V symmetrical output, providing a stable and reliable dual power supply for the operational amplifier. The circuit uses a filter network to effectively suppress high-frequency noise and, in conjunction with a low-dropout linear regulator, achieves a precise ±12V output. It also features overcurrent protection and reverse connection protection.

[0047] like Figure 7 As shown, the host computer software is based on Figure 2The derived measurement equation enables intelligent data processing, and the insulation resistance parameters are solved in real time through calculation. It integrates data visualization, abnormal alarm and historical storage functions. According to actual measurements, the single calculation time is less than 200ms and the measurement accuracy deviation is less than 5%.

[0048] like Figure 8 The figure shows the measured data. The excitation source voltage is stable within the range of 750V±5V. The measured value of the insulation resistance between the positive electrode and ground is 8.99MΩ±0.45MΩ (nominal value 9MΩ), and the measured value of the insulation resistance between the negative electrode and ground is 2.01MΩ±0.10MΩ (nominal value 2MΩ).

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Those skilled in the art will recognize that the present invention can be modified and varied in many ways. Several improvements and modifications can be made without departing from the original inventive technology, and all such modifications should be included within the scope of protection of the present invention.

Claims

1. A dynamic monitoring and adaptive balancing device for insulation impedance in a power system based on GD32, characterized in that, include: Step 1: The GD32 microcontroller is used to acquire the insulation parameters of the power system in real time and execute the dynamic impedance balance algorithm. Step 2, the signal conditioning circuit is connected to the ADC input of the GD32 and is used to filter, amplify and level-convert the insulation impedance signal; Step 3, Dynamic impedance adjustment module, including solid-state relay drive circuit and multi-level resistor array, controlled by GD32 to adjust system impedance; Step 4, Communication module, supports CAN bus, used to upload monitoring data and receive external control commands; Step 5, the power management module, provides a stable power supply to the system and has overvoltage / overcurrent protection functions.

2. The apparatus according to claim 1, characterized in that, In step 1, the GD32 microcontroller uses a multi-step state machine control algorithm to realize dynamic impedance measurement, acquires voltage data in a time-division manner through optocoupler switching, and calculates insulation resistance based on an improved voltage divider calculation model. At the same time, a hardware-level average filtering algorithm and a range limiting mechanism are used to ensure measurement accuracy and reliability.

3. The apparatus according to claim 1, characterized in that, In step 2, the signal conditioning module includes an LC-π filter to suppress common-mode noise in order to adapt to the signal bandwidth requirements under different operating conditions.

4. The dynamic impedance adjustment device according to claim 1, characterized in that, In step 3, the solid-state relay driving circuit can achieve millisecond-level response to quickly switch the resistors in the multi-level resistor array.

5. The dynamic impedance adjustment device according to claim 1, characterized in that, In step 3, the control unit is a GD32 microcontroller, which is used to precisely control the solid-state relay drive circuit to achieve dynamic adjustment of the system impedance.

6. The CAN communication circuit according to claim 1, characterized in that, In step 4, the circuit includes a B0505S type isolation power supply to isolate the input voltage from the output voltage, and a 120Ω terminating resistor to match the standard characteristic impedance of the CAN bus.

7. The apparatus according to claim 1, characterized in that, In step 5, the power module integrates multiple protection circuits to effectively prevent damage to the system caused by abnormal operating conditions such as overvoltage and overcurrent.

8. The apparatus according to any one of claims 1 to 7, characterized in that, The device achieves real-time monitoring and dynamic balance of the insulation impedance of the power system through the coordinated operation of its various modules.

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