Data driven modeling for power transformer lifespan evaluation

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation’s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

Impact of high PV penetration on distribution transformer life time

Integration of rooftop photovoltaics (PVs) in residential networks at moderate penetration levels is becoming a reality in many countries including Australia. Despite the technical challenges in properly accommodating PV units, one of the major benefits is the ability of PV units to extend useful life time of distribution transformers. This effect is not quantified in the existing literature. This paper carries out an analysis into the impacts of rooftop PVs at different penetration levels on the performance of distribution transformers and residential networks. This paper presents a methodology to quantify the benefit of the distribution transformer life extension brought about by customer-owned rooftop PV units. The proposed methodology is applied to a real distribution system with various scenarios, including different penetration levels. The results show the distribution transformer loss-of-life function, as a function of the rooftop PV penetration level, is monotonically decreasing function which saturates after a certain penetration level. The best life improvements occur with transformers that are highly loaded and the presence of a significant PV installation may support the deferral of transformer upgrades.

Multi-agent systems for modeling high penetration photovoltaic system impacts in distribution networks

This paper reviews the use of multi-agent systems to model the impacts of high levels of photovoltaic (PV) system penetration in distribution networks and presents some preliminary data obtained from the Perth Solar City high penetration PV trial. The Perth Solar City trial consists of a low voltage distribution feeder supplying 75 customers where 29 consumers have roof top photovoltaic systems. Data is collected from smart meters at each consumer premises, from data loggers at the transformer low voltage (LV) side and from a nearby distribution network SCADA measurement point on the high voltage side (HV) side of the transformer. The data will be used to progressively develop MAS models.