Traditional transformer monitoring equipment requires invasive probes to be submerged in the oil. New non-intrusive condition monitoring equipment which monitors the transformer`s voltage and current and uses a self-learning algorithm to predict future faults and identify losses.
Most of the current condition monitoring equipment require invasive probes to be installed, which lead to long outages and complex oil extraction processes. This all contributes to the high cost of installation. A new type of condition monitoring equipment has been created which involves using the measurements from Current Transformers (CT’s) and Voltage Transformers (VT’s). A self-learning algorithm is then used to establish the existence of any minute losses and their source. This technology can also be used to predict developing faults and identify a remedial solution, therefore extending the life of the asset.
This project will consist of the trial of the Engaging Online Transformers Monitoring Solution on eligible UKPN assets, in order to:
The diagnostic system to be implemented is based exclusively on the acquisition of electrical quantities (voltages and currents of the transformers windings), being completely non-invasive and of easy practical installation.
With this diagnostic system, failures in transformer windings and core (short-circuits between turns, degradation of the insulation system of the windings and hot spots in the ferromagnetic core), and some faults in tap-changers can be detected. The processing module of the diagnostics system will be connected to an Ethernet network, the results will be accessible through the TransfoView software, which stores the information collected and processed by the diagnostic system.
By installing the novel condition monitoring equipment, developing faults inside the transformer will become visible and thus preventable. As a result of the non-intrusive nature of this solution, it can be scaled with minimal business disruption. This will unlock more accurate business planning and maximising asset operation. Additionally, the project sets out to investigate if the new information collected can enable a more efficient transformer maintenance programme.