Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer

thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies. However

existing prediction methods based on the structure of ‘splicing prediction and diagnosis method’ suffer from limitations such as inability to achieve global optimality

error accumulation

and low prediction accuracy. To fill this gap

a novel direct prediction method of a transformer state based on knowledge and data fusion-driven model (K&DFDM) is proposed in this paper. Firstly

a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time

encompassing online monitoring

offline testing

evaluation results

and actual operation data. After that

correlation knowledge between state quantities

fault diagnosis mechanism knowledge

current diagnosis experience knowledge

and uncertain fuzzy knowledge are extracted separately. The actual fault mechanism

existing expert experience

and other knowledge in the diagnosis process are quantified. Then

the attention model is subsequently optimised

leveraging quantitative knowledge to effectively constrain and guide the data prediction process. Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction. The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum. The verification results

comprising 327 cases

demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods

leading to a direct state prediction accuracy of 96.33%.