Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a...Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.展开更多
基金supported by Shandong Provincial Natural Science Foundation(ZR2024ME229,ZR2024ZD29).
文摘Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.
