The reliable supply of energy has never been more important than it is today. In a hyper-connected world, the continuous provision of power is critical to almost every aspect of our lives. Any disruption or failure in supply can be serious cause for concern.
There is nothing like a power outage to make us appreciate just how much we depend on reliable electricity, from comfort and connections, to health and safety. But beyond the immediate impact, a power blackout can have a significant impact on the economy. In fact, recent blackouts in Australia cost businesses an estimated $367 million, according to the state’s peak business lobby.
For the utilities, the high cost of equipment downtime, particularly during peak demand, means every step must be taken to ensure preventable equipment failure is avoided. Thankfully, with modern predictive analytics tools, utilities have a new way of anticipating when failures are likely to occur and maximising smooth operations.
By providing early warnings of abnormal operating conditions that may otherwise go unnoticed, predictive analytics are able to give early warnings of equipment failure, where patterns of abnormality that lead to failure can be identified and avoided or responded to quickly. The analysis of large amounts of available data allows utilities to overcome disruptive obstacles such as power failures or unit shutdowns as they are able to anticipate where failures are likely to occur ahead of time.
These tools have the power to transform raw data into easy-to-understand insights that improve availability, reliability and decision-making. They work by monitoring the performance of utilities’ assets and creating a model of ‘normal’ operating behaviour. Based on historical norms, it compares that model to real-time operating conditions by alerting the system when data is deviating from the predicted norm.
Early knowledge of an expected problem allows utilities to effectively assess the situation and determine a controlled outcome. Modern digital technology, for example sensors and prediction engines, are capable of providing key insights into operations by overseeing asset performance, sending notifications about asset degradation, and recommending actionable maintenance information.
Utilities can also find savings in the reduced cost of maintenance. With predictive analytics, risk assessment becomes close to an exact science as the behaviours of each asset can be observed to better prioritise capital and operational expenditure. Cost savings include the focusing of efforts on the most vulnerable and highest priority assets, as well as the lowered costs of loss of power, lost productivity and additional labouring all associated with emergency fixes that occur outside of a planned outage.
Overall, predictive analytics tools allow utilities to monitor critical assets for the purpose of identifying, diagnosing and prioritising equipment problems – continuously and in real time. The reliability and efficiency improvements that come with the use of predictive analytics tools also result in increased customer satisfaction rates – understandably as they can experience more reliable service with fewer outages – as utilities now have the insight needed to avoid potential equipment failure and forced outages.
To learn more about predictive analytics tools, download Schneider Electric’s ‘Understanding How Predictive Analytics Tools Benefit Power Utility Asset Management’ whitepaper.April 19, 2017