In order to rectify the problem of measurement inaccuracies in secondary protection measurement circuits at substations

which undermine measurement precision

diminish protection sensitivity

and consequently impair the reliable operation of protective devices

this paper puts forth a multi-channel data verification and evaluation methodology founded on a bidirectional long short-term memory (BiLSTM) network that has been refined through a genetic algorithm (GA). This proposed methodology aims to guarantee measurement accuracy and to augment the usability of multi-channel information. An initial dataset is established based on relative deviations between multi-channel data groups

followed by the formulation of corresponding classification principles. Case study analysis demonstrates that compared to other network models

the GA-BiLSTM multi-channel evaluation model achieves an accuracy rate of 97%

representing a significant improvement.