The invention aims at the characteristics of randomness and suddenness of
power grid equipment faults. Therefore, the problem is difficult to accurately predict; the invention provides a method for enhancing operation and
maintenance management and control of distribution network equipment. The invention belongs to the technical field of
power grid equipment operation.
Big data technologies such as clustering analysis,
machine learning and the like are applied to effectively integrate and mine
system data such as an
electricity consumption
information acquisition system, PMS2.0, D5000 and thelike; data islands among the systems are broken through, an equipment fault prediction model is established according to an adjacent
propagation effect analysis theory, the equipment fault occurrenceprobability can be pre-judged in advance, equipment maintenance can be arranged in advance, the power failure frequency and time are reduced, the power supply quality is improved, and the operation and maintenance level of a distribution network is greatly improved. According to the method, an
alarm signal is sent out before an equipment fault occurs, the equipment fault is pre-judged in advance,a coping strategy is provided, the
big data technology is innovatively applied to predict the possibility that the equipment has faults in future according to historical fault records, and a passive repair working mode is changed into an active repair working mode.