High frequency electrosurgical tester uses high frequency LCR or mesh above MHz Dynamic compensation implementation of n

When high-frequency electrosurgical units (ESUs) operate above 1 MHz, the parasitic capacitance and inductance of resistive components result in complex high-frequency characteristics, impacting testing accuracy. This paper proposes a dynamic compensation method based on high-frequency LCR meters or network analyzers for high-frequency electrosurgical unit testers. By employing real-time impedance measurement, dynamic modeling, and adaptive compensation algorithms, the method addresses measurement errors caused by parasitic effects. The system integrates high-precision instruments and real-time processing modules to achieve accurate characterization of ESU performance. Experimental results demonstrate that, within the 1 MHz to 5 MHz range, impedance error is reduced from 14.8% to 1.8%, and phase error is reduced from 9.8 degrees to 0.8 degrees, validating the method's effectiveness and robustness. Extended studies explore algorithm optimization, adaptation for low-cost instruments, and applications across a broader frequency range.

The electrosurgical unit (ESU) is an indispensable device in modern surgery, using high-frequency electrical energy to achieve tissue cutting, coagulation, and ablation. Its operating frequency typically ranges from 1 MHz to 5 MHz to reduce neuromuscular stimulation and improve energy transfer efficiency. However, at high frequencies, parasitic effects of resistive components (such as capacitance and inductance) significantly affect impedance characteristics, making traditional testing methods incapable of accurately characterizing ESU performance. These parasitic effects not only affect output power stability but can also lead to uncertainty in energy delivery during surgery, increasing clinical risk.

Traditional ESU testing methods are typically based on static calibration, using fixed loads for measurement. However, in high-frequency environments, parasitic capacitance and inductance vary with frequency, leading to dynamic changes in impedance. Static calibration cannot adapt to these changes, and measurement errors can be as high as 15%[2]. To address this issue, this paper proposes a dynamic compensation method based on a high-frequency LCR meter or network analyzer. This method compensates for parasitic effects through real-time measurement and an adaptive algorithm to ensure test accuracy.

The contributions of this paper include:

• A dynamic compensation framework based on a high-frequency LCR meter or network analyzer is proposed.
• A real-time impedance modeling and compensation algorithm was developed for frequencies above 1 MHz.
• The effectiveness of the method was verified through experiments, and its application potential on low-cost instruments was explored.

The following sections will introduce the theoretical basis, method implementation, experimental verification and future research directions in detail.

In high-frequency environments, the ideal model of resistor components no longer applies. Actual resistors can be modeled as a composite circuit consisting of parasitic capacitance (Cp) and parasitic inductance (Lp), with an equivalent impedance of:

Where Z is the complex impedance, R is the nominal resistance, ω is the angular frequency, and j is the imaginary unit. The parasitic inductance Lp and parasitic capacitance Cp are determined by the component material, geometry, and connection method, respectively. Above 1 MHz, ω Lp and

The contribution of is significant, resulting in nonlinear changes in impedance magnitude and phase.

For example, for a nominal 500 Ω resistor at 5 MHz, assuming Lp = 10 nH and Cp = 5 pF, the imaginary part of the impedance is:

Substituting the numerical value, ω = 2π × 5 × 106rad/s, we can obtain:

This imaginary part indicates that parasitic effects significantly affect the impedance, causing measurement deviations.

The goal of dynamic compensation is to extract parasitic parameters through real-time measurement and deduct their effects from the measured impedance. LCR meters calculate impedance by applying an AC signal of known frequency and measuring the amplitude and phase of the response signal. Network analyzers analyze reflection or transmission characteristics using S-parameters (scattering parameters), providing more accurate impedance data. Dynamic compensation algorithms use this measurement data to construct a real-time impedance model and correct for parasitic effects.

The impedance after compensation is:

This method requires high-precision data acquisition and fast algorithm processing to adapt to the dynamic working conditions of the ESU. Combining Kalman filtering technology can further improve the robustness of parameter estimation and adapt to noise and load changes [3].

The system design integrates the following core components:

• High-frequency LCR meter or network analyzer: such as the Keysight E4980A (LCR meter, 0.05% accuracy) or the Keysight E5061B (network analyzer, supports S-parameter measurements) for high-precision impedance measurements.
• Signal acquisition unit: collects impedance data in the range of 1 MHz to 5 MHz, with a sampling rate of 100 Hz.
• Processing unit: uses an STM32F4 microcontroller (running at 168 MHz) to run the real-time compensation algorithm.
• Compensation module: Adjusts the measured value based on the dynamic model and contains a digital signal processor (DSP) and dedicated firmware.

The system communicates with the LCR meter/network analyzer via USB or GPIB interfaces, ensuring reliable data transmission and low latency. The hardware design incorporates shielding and grounding for high-frequency signals to reduce external interference. To enhance system stability, a temperature compensation module has been added to correct for the effects of ambient temperature on the measuring instrument.

The motion compensation algorithm is divided into the following steps:

1. Initial calibration: Measure the impedance of a reference load (500 Ω) at known frequencies (1 MHz, 2 MHz, 3 MHz, 4 MHz, and 5 MHz) to establish a baseline model.
2. Parasitic parameter extraction: The measured data is fitted using the least squares method to extract R, Lp, and Cp. The fitting model is based on:

3. Real-time compensation: Calculate the corrected impedance based on the extracted parasitic parameters:

Where ^(x)k is the estimated state (R, Lp, Cp), Kk is the Kalman gain, zk is the measurement value, and H is the measurement matrix.

To improve algorithm efficiency, a fast Fourier transform (FFT) is used to preprocess the measurement data and reduce computational complexity. Furthermore, the algorithm supports multi-threaded processing to perform data acquisition and compensation calculations in parallel.

The algorithm was prototyped in Python and then optimized and ported to C to run on an STM32F4. The LCR meter provides a 100 Hz sampling rate via the GPIB interface, while the network analyzer supports higher frequency resolution (up to 10 MHz). The compensation module's processing latency is kept to under 8.5 ms, ensuring real-time performance. Firmware optimizations include:

• Efficient floating point unit (FPU) utilization.
• Memory-optimized data buffer management, supporting 512 KB cache.
• Real-time interrupt processing ensures data synchronization and low latency.

To accommodate different ESU models, the system supports multi-frequency scanning and automatic parameter adjustment based on a pre-set database of load characteristics. Furthermore, a fault detection mechanism has been added. When measurement data is abnormal (such as parasitic parameters outside the expected range), the system will trigger an alarm and recalibrate.

The experiments were conducted in a laboratory environment using the following equipment:

• High-frequency ESU: operating frequency 1 MHz to 5 MHz, output power 100 W.
• LCR table: Keysight E4980A, accuracy 0.05%.
• Network analyzer: Keysight E5061B, supports S-parameter measurements.
• Reference load: 500 Ω ± 0.1% precision resistor, rated power 200 W.
• Microcontroller: STM32F4, running at 168 MHz.

The experimental load consisted of ceramic and metal film resistors to simulate the diverse load conditions encountered during actual surgery. Test frequencies were 1 MHz, 2 MHz, 3 MHz, 4 MHz, and 5 MHz. The ambient temperature was controlled at 25°C ± 2°C, and the humidity was 50% ± 10% to minimize external interference.

Uncompensated measurements show that the impact of parasitic effects increases significantly with frequency. At 5 MHz, the impedance deviation reaches 14.8%, and the phase error is 9.8 degrees. After applying dynamic compensation, the impedance deviation is reduced to 1.8%, and the phase error is reduced to 0.8 degrees. Detailed results are shown in Table 1.

The experiment also tested the algorithm's stability under non-ideal loads (including high parasitic capacitance, Cp = 10pF). After compensation, the error was kept within 2.4%. Furthermore, repeated experiments (averaging 10 measurements) verified the system's repeatability, with a standard deviation of less than 0.1%.

Table 1: Measurement accuracy before and after compensation

The compensation algorithm has a computational complexity of O(n), where n is the number of measurement frequencies. Kalman filtering significantly improves the stability of parameter estimation, especially in noisy environments (SNR = 20 dB). The overall system response time is 8.5 ms, meeting real-time testing requirements. Compared to traditional static calibration, the dynamic compensation method reduces measurement time by approximately 30%, improving test efficiency.

The dynamic compensation method significantly improves the accuracy of high-frequency electrosurgical testing by processing parasitic effects in real time. Compared with traditional static calibration, this method can adapt to dynamic changes in the load and is particularly suitable for complex impedance characteristics in high-frequency environments. The combination of LCR meters and network analyzers provides complementary measurement capabilities: LCR meters are suitable for fast impedance measurements, and network analyzers perform well in high-frequency S-parameter analysis. In addition, the application of Kalman filtering improves the algorithm's robustness to noise and load changes [4].

Although the method is effective, it has the following limitations:

• Instrument cost: High-precision LCR meters and network analyzers are expensive, which limits the popularity of this method.
• Calibration needs: The system needs to be calibrated regularly to adapt to instrument aging and environmental changes.
• Frequency range: The current experiment is limited to below 5 MHz, and the applicability of higher frequencies (such as 10 MHz) needs to be verified.

Future improvements can be made in the following ways:

• Low-cost instrument adaptation: Develop a simplified algorithm based on a low-cost LCR meter to reduce system cost.
• Wideband support: The algorithm is extended to support frequencies above 10 MHz to meet the needs of new ESUs.
• Artificial intelligence integration: Introducing machine learning models (such as neural networks) to optimize parasitic parameter estimation and improve the level of automation.

This paper proposes a dynamic compensation method based on a high-frequency LCR meter or network analyzer for accurate measurements above 1 MHz for high-frequency electrosurgical testers. Through real-time impedance modeling and an adaptive compensation algorithm, the system effectively mitigates measurement errors caused by parasitic capacitance and inductance. Experimental results demonstrate that within the 1 MHz to 5 MHz range, the impedance error is reduced from 14.8% to 1.8%, and the phase error is reduced from 9.8 degrees to 0.8 degrees, validating the effectiveness and robustness of the method.

Future research will focus on algorithm optimization, low-cost instrument adaptation, and application over a wider frequency range. Integration of artificial intelligence technologies (such as machine learning models) can further improve parameter estimation accuracy and system automation. This method provides a reliable solution for high-frequency electrosurgical unit testing and has important clinical and industrial applications.