A Modular Architecture for Real-time Monitoring & Diagnosing of Low Voltage Network Assets
The electricity that powers home appliances, glow your lights and charge your phones, hops through multiples networks in order to reach your home. Following diagram shows these networks and highlight the low voltage (230V/415V) distribution network which we would focus on this discussion.
Low voltage (LV) network starts its head end from the distribution transformer (DT) and carries 230V single phase or 415V three phase and reaches your home or business. Electricity flows from DT on power-line conductors (wires) which mounted or strung on wooden, steel or reinforced concrete pole structures. Usually the low voltage network has 4 connectors.
Health of the distribution transformer plays a major role in resilience of the LV network. Accelerated degradation and failure of distribution transformers can occur because of several conditions such as oil leakage, overloading, unbalanced loading and harmonics. However, the majority of failures are caused by a combination of these electrical, mechanical and thermal stresses acting upon the power transformer components over time. Impact on service life is non-binary and multi-dimensional in nature. Usually there is enough prior signs from the gradual breakdowns of transformer components that leads to eventual transformer failure.
Following table shows the failure modes of the transformer.
Health status of the DT can be determined and predicted based on the following parameters shown on the below diagram.
These parameters can be measured using sensors and real-time data feed is aggregated using an IoT device which can feed the cleansed aggregated data in to a cloud data platform.
Following component architecture is suggested for the above use case.
An IoT device fitted to each transformer works as an edge processing gateway. It publishes cleansed and categorised data real-time to a cloud deployed data platform. IoT device performs data capture while the cloud data platform play the role of processing of data to identify patterns and apply predictive analytics to improve and optimise inspection and maintenance strategies which contributes to increased network reliability.
Cloud data platform will query streaming data in real time and get up-to-date information on on the network. It can be augmented with machine learning.
As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. A typical data flow is shown below.
Monitoring and eventing model would like below.