In this article, we explore how Neuron Soundware’s advanced AI and IoT solution offer a transformative approach to identifying and mitigating potential issues in transformers, thus preventing unplanned downtime and reducing maintenance costs.
Transformers are crucial components in electrical power systems, responsible for converting voltage levels to meet various application needs. Despite their importance, they are prone to issues such as insulation degradation and mechanical failures. These problems can cause partial discharges (PD) and lead to transformer outages. Unplanned downtime and the associated maintenance costs can be significant, impacting the financial bottom line and the reliability of the power supply.
The monitoring process starts with the installation of acoustic sensors on the transformers. These sensors continuously capture acoustic signals, which are processed locally on Neuron Soundware’s nEdge™ IoT devices. The data is then analyzed using advanced AI algorithms to identify any anomalies. The system’s machine learning models are trained to recognize normal acoustic patterns and detect deviations that indicate potential issues.
One of the critical aspects of this monitoring system is its ability to detect partial discharges (PD), which are caused by defects in the insulation due to impurities, high stress, or degradation. The occurrence of PD can be random, depending on the electric field stress and environmental conditions. Neuron Soundware’s technology continuously monitors PD occurrences, providing data on their frequency and intensity, which are crucial indicators of transformer health.
Implementing Neuron Soundware’s transformer monitoring solution offers numerous benefits, beginning with the early detection of partial discharges and other anomalies. This early detection allows for timely maintenance, significantly reducing the risk of catastrophic failures and unplanned downtime. Consequently, this proactive maintenance approach enhances the reliability of power distribution and extends the lifespan of transformers.
Continuous monitoring provides a comprehensive understanding of transformer health, which facilitates more efficient maintenance planning. By targeting transformers that show signs of deterioration, companies can optimize maintenance schedules, reduce unnecessary expenditures, and improve operational efficiency. Additionally, the technology supports better inventory management and reduces costs for spare parts through informed decision-making.
Unlike traditional monitoring methods such as annual chemical analysis of transformer oil—which can detect the presence of partial discharges through residual gasses (mainly hydrogen) but not determine the exact timing—continuous monitoring allows not only early detection of partial discharges but also specifies when they occurred. This precise information enables more accurate maintenance and operational decisions.
Preventive replacement strategies can be refined as well. Instead of replacing transformers at the end of their stated service life, transformers can be used until they show signs of defects severe enough to endanger operation and safety. This approach maximizes the full lifespan of the transformer.
In recent years, the cost of power transformers has been rising due to several factors. One primary reason is the increasing demand driven by the global push for electrification and the integration of renewable energy sources. This trend has led to higher prices for raw materials and increased manufacturing costs. Thus, optimizing transformer usage and maintenance becomes even more critical in managing these escalating expenses.