Condition assessment is the key link in the condition maintenance and health management of the power equipment. Taking 220 kV oil-immersed transformers as the research object

we proposed an insulation condition assessment method based on improved extension cloud theory with optimal cloud entropy. Firstly

aiming at the randomness and fuzziness problem of insulation condition level boundary information

we constructed a basic theoretical framework of condition assessment based on extension cloud theory. Afterwards

considering the clarity and ambiguity of the grade division

we proposed a calculation method of optimal cloud entropy that can be adaptive to the evaluation object

and realized the improvement of the traditional extension cloud theory. Finally

in view of the shortcomings of subjective weighting method and objective weighting method

a combination weighting model based on fuzzy set-valued statistical method and entropy weight method was constructed to realize the dynamic adjustment of the index weight based on the degree of information change. Case studies and comparison analysis with traditional methods show that the proposed method can be adopted to effectively deal with the uncertainty factors in the insulation condition assessment

and its evaluation results are closer to the actual state of equipment

so the proposed method has a certain application feasibility.