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This p aper discusses th e de tails 0 f de signing and bu ilding a monitoring system that can be us ed to evalu ate th e status of power system apparatus remotely. The mo nitoring system was built and tested to mon itor both the part ial discharge inside a transformer m odel and the leakag e curren ton an outdoor insulator's surface. The m onitoring sy stem is des igned to measure di fferent parameters and hence to evaluate the status of th e po wer sy stem apparatus and di splay th em in real time. Moreover, the system will also send email notifications which keep the utili ty engineer informed regarding the stat us of the power system apparatus at all times. INTRODUCTIONIn ac cordance to the global co ncerns threatening the environment of f ossil-fuel electricity generation, and the increasing bl ackouts in current g rids due to the aged power system infrastructure, the world is heading toward developing a more intelligent gr id infrastructure, known as the Smart Grid (SG). The SG promotes the deployment of re newable energy sources (RESs) to mi tigate th e climate cha nge, reduce t he depe ndence on foss ii-fuel based e lectricity generation a nd to sa tisfy the e nergy demand growth. According to the Smart Grids European Technology platform, the SG is defined as "an electricity network that can intelligently integrate the actions of all users connected to it -generators, consumers and those that do b oth, in order to efficiently de liver sustainable, economic an d se cure electricity supply" [1]. To accomplish th e potential goals of S Gs, one of the smart technologies th at ca n be utilized in the SG in frastructure is t he re mote m onitoring 0 f p ower sys tern appa ratuses (PSAs).Conventional m onitoring m ethods re quire t he presence of the site engineer or utility worker to perform the required dia gnosis of the PSA. This is disadvantageous in t he sense t hat the apparatus i s monitored only periodically. Although periodic checkups could be som etimes su fficient, in some cases significant behaviors or abrupt changes within the P SA, could not take place during those che ckups and might lead to its failure. Those fa i1ures could be ca tastrophic and mig ht cause su dden power out ages an d significant losses. In addition, som e conventional monitoring methods require a tern porary shu tdown of the apparatus, which could be costly and i nconvenient t 0 the consumers. These disadvantages of on-site monitoring could be avoided by 978-1-4673-0862-5/12/$31.00 ©2012 IEEE deploying re mote mo nitoring technology of t he P SA in future SGs.Remote monitoring 0 f pow er system components promotes a more reliable, cost-effective and smarter grid. In remote monitoring, sma rt sensors are em ployed to monitor the PSAs. These sensors m easure ce rtain phenomenon or behavior within the PSA that can be used as an in dicator to its co ndition or performance. With the Information a nd Com munication Tec hnology (lCT) deployment in the S G, th ose m easurements c an b e instantly accessed by the utility through the inte...

This paper explores a holistic approach to characterize trouble stages by applying automated event recognition of abnormal pressure increases and associating those events to formation and operational causes. This analysis of pressure increases provides insight into the potential causes of operational difficulties, and the related diagnostics can suggest improvements to future pump schedules. Improving how stages are pumped is profitable both in the short-term (reducing wasted fluid and chemicals, and other remediation measures) and in the long-term (increased well productivity). Quantifying how design decisions ultimately affect operations can help decrease the frequency of operational problems and help realize these gains. In this study, the identification of problematic frac stages was initially performed manually (stage-by-stage) using a cloud-based hydraulic fracture data application. During this process, the team recognized that the problem stages had their own characteristic pressure signature - a sudden unexplained pressure increase in the absence of rate changes. A machine learning algorithm was then developed to automatically identify this type of signature. Additionally, previously published machine learning algorithms were used to recognize other operational events of interest, e.g., when proppant reaches the perforations. Then by combining the various events and creating short search windows around each abnormal pressure increase, it is possible to find concurrent operations that may be associated with the observed pressure behavior. A subsequent statistical analysis revealed that abnormal pressure increases often coincided with changes in proppant concentration in problem stages (stages with abnormal treating pressure behavior). This behavior may be due to near-wellbore effects caused by the changing fluid flow dynamics. Furthermore, it was observed that treating pressures that behaved contrary to hydrostatic pressure effects may be useful in identifying when injectivity is lost and provide an early signal for screen outs. Through this holistic approach, we were able to identify trouble stages and discern some diagnostics for automated detection of abnormal treating pressure increases. The team was able to identify areas within the stages that were inefficiently pumped, resulting in cost-savings through optimization of proppant and friction reducer (FR) loadings while maintaining a level of caution to prevent screen outs. Finally, the automated detection of pressure anomalies offers a pathway to the real-time prediction and avoidance of operational difficulties such as pressure outs and screen outs.

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