Volume 3 · Issue 9 (2026)
DOI number:
10.66521/2938-9933-2026092101
Influence of Load-Impedance Variation on the Output-Power Characteristics of an Electrosurgical Generator and an Adaptive Compensation Method
Weiguang Niu, Mengfan Li, Kaixin Hou
People's Liberation Army Air Force 986 Hospital, Xi'an Shanxi 710032, China
Abstract: Load-impedance variation causes the power delivered by an electrosurgical generator to deviate from its set value. This study developed a variable resistive-load test and a two-input power-compensation method for a Shanghai Hutong GD350-B generator. Monopolar pure-cut and coagulation modes were evaluated from 50 to 1000 Ω. Pure-cut set powers were 60, 120, and 180 W, whereas coagulation set powers were 30, 60, and 90 W. Each condition was repeated three times. Relative power gain was modeled using a second-order response surface, a third-order polynomial, and a 2-10-6-1 back-propagation neural network. Generalization was assessed using fivefold cross-validation grouped by impedance–set-power condition and independent off-grid resistances of 150, 350, 550, 750, and 950 Ω. In both modes, output power approached the set value at intermediate resistances but decreased at the low- and high-resistance boundaries. The interaction between resistance and set power was significant. The cross-validated mean absolute percentage errors of the BP model were 1.57% for pure cut and 1.91% for coagulation. At the independent resistance points, BP inverse compensation reduced the pooled mean absolute percentage error from 8.38% to 1.66%, an 80.2% reduction. The proportion of measurements within ±5% error increased from 40.0% to 96.7%. Mode-specific compensation based on load resistance and set power therefore improved steady-state power consistency. The BP model provided the highest accuracy, whereas the third-order polynomial offered a transparent alternative for resource-constrained embedded implementation.
Keywords: Electrosurgical generator; Load resistance; Output power; Response surface; Back-propagation neural network; Adaptive compensation
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