High frequency electrosurgical tester uses high frequency LCR or mesh above MHz Dynamic compensation implementation of network analyzer
High frequency electrosurgical tester uses high frequency LCR or mesh above MHz Dynamic compensation implementation of network analyzer
Single Super 1 Qiang Xiaolong 2 Zhang Chao 3 * Liu Jiming 3
(1. Heilongjiang Provincial Institute for Drug Control 2. Guangxi Zhuang Autonomous Region Medical Device Testing Center 3. Dongguan Jingbang Machinery Technology Co., Ltd.)
Abstract : When a high-frequency electrosurgical unit (ESU) operates above 1 MHz, the parasitic capacitance and inductance of the resistive components lead to complex high-frequency characteristics, affecting test accuracy. This paper proposes a dynamic compensation method for high-frequency electrosurgical unit testers based on a high-frequency LCR meter or network analyzer. Real-time impedance measurement, dynamic modeling, and an adaptive compensation algorithm are used to address measurement errors caused by parasitic effects. The system integrates high-precision instruments and real-time processing modules to accurately characterize ESU performance. Experimental results show 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, verifying the effectiveness and robustness of the method. Further research explores the application prospects of algorithm optimization, low-cost instrument adaptation, and a wider frequency range.
Dynamic Compensation Implementation for High-Frequency Electrosurgical Unit Testing Using High-Frequency LCR or Network Analyzers Above MHz
Shan Chao 1 ,Qiang Xiaolong 2 ,Zhang Chao 3 ,Liu jiming 3 .
(1. Heilongjiang Institute for Drug Control, Harbin 150088, China ; 2. Guangxi Zhuang Autonomous Region Medical Device Testing Center, Nanning 530021, China ; 3. Kingpo Technology Development Limited Dongguan 5 23869 ; China )
Abstract: 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.
introduction
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.
Theoretical analysis
High frequency resistance characteristics
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.
Dynamic compensation principle
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].
method
System Architecture
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.
Motion compensation algorithm
The motion compensation algorithm is divided into the following steps:
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.
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:
Real-time compensation
: Calculate the corrected impedance based on the extracted parasitic parameters:
Iterative adjustment
: Optimize parameter estimation through Kalman filtering to adapt to dynamic changes in load. The state update formula of Kalman filtering is:
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.
Implementation details
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.
Experimental verification
Experimental setup
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.
Experimental results
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
frequency ( MHz ) | Uncompensated impedance error (%) | Impedance error after compensation (%) | Phase error ( Spend ) |
1 | 4.9 | 0.7 | 0.4 |
2 | 7.5 | 0.9 | 0.5 |
3 | 9.8 | 1.2 | 0.6 |
4 | 12.2 | 1.5 | 0.7 |
5 | 14.8 | 1.8 | 0.8 |
Performance Analysis
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.
discuss
Method advantages
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].
limitation
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.
Optimization direction
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.
in conclusion
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.
References
GB9706.202-2021 "Medical electrical equipment - Part 2-2: Particular requirements for the basic safety and essential performance of high-frequency surgical equipment and high-frequency accessories" [S]
JJF 1217-2025. High-Frequency Electrosurgical Unit Calibration Specification
[S]
Chen Guangfei. Research and design of high-frequency electrosurgical analyzer[J]. Beijing Biomedical Engineering, 2009, 28(4): 342-345.
[4]Huang Hua, Liu Yajun. Brief analysis of the power measurement and acquisition circuit design of QA-Es high-frequency electrosurgical analyzer[J]. China Medical Equipment, 2013, 28(01): 113-115.
[5] Chen Shangwen, Performance testing and quality control of medical high-frequency electrosurgical unit[J]. Measuring and Testing Technology, 2018, 45(08): 67~69.
[6] Chen Guangfei, Zhou Dan. Research on calibration method of high-frequency electrosurgical analyzer[J]. Medical and Health Equipment, 2009, 30(08): 9~10+19.
[7] Duan Qiaofeng, Gao Shan, Zhang Xuehao. Discussion on high-frequency leakage current of high-frequency surgical equipment. J. China Medical Device Information, 2013, 19(10): 159-167.
[8] Zhao Yuxiang, Liu Jixiang, Lu Jia, et al., Practice and discussion of high-frequency electrosurgical unit quality control testing methods. China Medical Equipment, 2012, 27(11): 1561-1562.
[9] He Min, Zeng Qiao, Liu Hanwei, Wu Jingbiao (corresponding author). Analysis and comparison of high-frequency electrosurgical unit output power test methods [J]. Medical Equipment, 2021, (34): 13-0043-03 .
About the Author
Author profile: Shan Chao, senior engineer, research direction: medical device product quality testing and evaluation and related research.
Author profile: Qiang Xiaolong, deputy chief technician, research direction: active medical device testing quality evaluation and standardization research .
Author profile: Liu Jiming , undergraduate, research direction : measurement and control design and development .
Corresponding author
Table 1: Measurement accuracy before and after compensation
frequency ( MHz ) | Uncompensated impedance error (%) | Impedance error after compensation (%) | Phase error ( Spend ) |
1 | 4.9 | 0.7 | 0.4 |
2 | 7.5 | 0.9 | 0.5 |
3 | 9.8 | 1.2 | 0.6 |
4 | 12.2 | 1.5 | 0.7 |
5 | 14.8 | 1.8 | 0.8 |
Performance Analysis
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.
discuss
Method advantages
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].
limitation
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.
Optimization direction
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.
in conclusion
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.
References
GB9706.202-2021 "Medical electrical equipment - Part 2-2: Particular requirements for the basic safety and essential performance of high-frequency surgical equipment and high-frequency accessories" [S]
JJF 1217-2025. High-Frequency Electrosurgical Unit Calibration Specification
[S]
Chen Guangfei. Research and design of high-frequency electrosurgical analyzer[J]. Beijing Biomedical Engineering, 2009, 28(4): 342-345.
[4]Huang Hua, Liu Yajun. Brief analysis of the power measurement and acquisition circuit design of QA-Es high-frequency electrosurgical analyzer[J]. China Medical Equipment, 2013, 28(01): 113-115.
[5] Chen Shangwen, Performance testing and quality control of medical high-frequency electrosurgical unit[J]. Measuring and Testing Technology, 2018, 45(08): 67~69.
[6] Chen Guangfei, Zhou Dan. Research on calibration method of high-frequency electrosurgical analyzer[J]. Medical and Health Equipment, 2009, 30(08): 9~10+19.
[7] Duan Qiaofeng, Gao Shan, Zhang Xuehao. Discussion on high-frequency leakage current of high-frequency surgical equipment. J. China Medical Device Information, 2013, 19(10): 159-167.
[8] Zhao Yuxiang, Liu Jixiang, Lu Jia, et al., Practice and discussion of high-frequency electrosurgical unit quality control testing methods. China Medical Equipment, 2012, 27(11): 1561-1562.
[9] He Min, Zeng Qiao, Liu Hanwei, Wu Jingbiao (corresponding author). Analysis and comparison of high-frequency electrosurgical unit output power test methods [J]. Medical Equipment, 2021, (34): 13-0043-03 .
About the Author
Author profile: Shan Chao, senior engineer, research direction: medical device product quality testing and evaluation and related research.
Author profile: Qiang Xiaolong, deputy chief technician, research direction: active medical device testing quality evaluation and standardization research .
Author profile: Liu Jiming , undergraduate, research direction : measurement and control design and development .
Corresponding author
- KINGPO will meet you at the 92nd China International Medical Equipment (Autumn) Expo in 2025
- KingPo Delivers and Installs State-of-the-Art Dust Chamber in Korea, Enhancing Local Testing Capabilities
- Neutral Electrode Temperature-rise Tester: Ensuring Safety in Electrosurgery
- ISO 80369-7 Luer Gauge Checklist
- What are the implications for manufacturers transitioning from ISO 594 to ISO 80369-7?
- KINGPO 2024 R&D Results Report
- ISO 80369-7:2016 Connectors with 6% (Luer) taper for intravascular or hypodermic applications What is the ISO 80369-7 standard? What happened to ISO 594-1 and ISO 594-2?
- ISO 80369-3 Test Equipment LIst
- Understanding ASTM F2059 Fluid Flow Test: A Comprehensive Overview
- Medical Device Pressure Validation: Ensuring Accuracy and Reliability