MechChem Africa November 2018

⎪ Maintenance and asset management ⎪

Arveen Gobind of machine assessment and reliability technology specialist, Martec, a Pragma Group company, describes how IIoT technology is now being used in condition-based maintenance programmes to extend the lives of transformers and other critical plant equipment. Extending transformer life through condition-based solutions

L ivinginthepresenttimeinSouthAfrica where the economy is not at its best, all areas of public and private sectors are forced to tighten their collective belts on capital expenditure. This financial constraintistheresultofthecascadedeffectof reductions in monthly spend, which is leading industry to venture into extending asset life and to promote sustainability by implement- ing condition-based maintenance principles. Hospitals, data centres, communication infrastructure facilities and heavy industries alike are critically dependent on a reliable electric power supply. Power anddistribution transformers form an integral part of opera- tional sustainability, either by servicing a pro- duction lineor to supply residential networks. A largepackaging companywas experienc- ingproblems related tohighmoisture content and moderate dissolved gas analysis (DGA) gas production rates on some of its most critical transformers. These transformers are identified for replacement in the year 2020, but the client wanted to monitor these units, as a failure would pose significant risk on the sustainability of operations. A proactive deci- sion was made to eradicate this problem by implementinga condition-basedmaintenance programme facilitatedbyanautomaticandef- ficientin-timetransformermonitoringsystem. An effective protection system, comple- mented by monitoring and diagnostics of transformers, are the most important tech- niques for extending the life of a transformer. Based on the transformer failure statistics, the failure location analysis and previous oil analysis data, the insight to the transformer

winding condition was derived as most valu- able for an indicative health status. An effective monitoring and diagnostics system are able to process transformer fun- damentals, acquire, process and store data, andderive corrective actions basedon inputs, setpoints, alarms and algorithms. A system was installed based on an IIoT system designed to continuously monitor moisture and dissolved hydrogen in trans- former oil, utilising on-line DGA technology for local and remote monitoring with predic- tive analytics. Furthermore, winding and oil temperatures, relative saturation and load are measured for correlation and analytics. This system continuously monitors the transformer in real-timeandcompares sensor outputs from the transformer with the refer- ence values of a healthy transformer. Adapting to industry 4.0, an IIoT-based transformer monitoring system for power transformers has been designed, imple- mentedand tested. ACloudbasedapplication that is linked to an IIoT gateway via Ethernet is installed in this systemto timeously receive and store transformer data in an accessible database. After experiencing deviations from setpoints or any abnormality, immediate ac-

tion can be taken to prevent any catastrophic failures of power transformers. A trigger initiates an SMS notification ex- pressing thealarmcondition raised. Themoni- toring systemdetailsmay be accessed via any Internet connectionwithgiven authorisation. The successes gained through the imple- mentationof the transformer healthmonitor- ing system, provide operational advice on the loading of the transformer. Trends revealed heavy loading of the aged transformers in short durations, which triggered relative saturation alarms. The monitoring of moisture and oil tem- perature also permits planning for off-line dry-outs. The transformer DGA results are stable and there is some low-level gasing, which is indicative of low-level overheating under operational conditions. The monitoring technique allowed the condition risk levels to be assigned with confidence. This allows better prioritisation of those transformers that require the most urgent maintenance. Condition monitoring with predictive algorithms has an important role for a reli- able electrical supply, preventing unplanned outages and promoting reliability. q

A screen shots of the online dashboard showing in-time monitoring of the transformer.

Tackling unemployment through learnerships In response to increasing unemployment rates in South Africa, Engen is running various campaigns to recruit candidates for learnerships, based on business and industry needs. To date, the company has trained over 500 artisans and professional drivers, who are now licensed to trade in various fields. fields tohelpeducate andupskill school leav- ers and unemployed youth. A learnership is a work-based learning programme where classroom studies at a college or training centre are combinedwith practical on-the-job experience. Research shows that people learn far better when they can practise what they have been taught in the classroom, in a real workplace environment.

company or within the broader petroleum industry. “In this way, the introduction of learnerships is making a good contribution towards solving the skills shortage in the industry,” says Mmalenyalo Galane, skills development facilitator at Engen. Galane says the learnerships offered by Engen form part of a nationally recognised qualification and are directly linked to an occupation. “A learnership not only teaches skills for a particular job; once training is complete, it also forms part of a higher quali- fication towards which learners can study by undertaking other learnerships or short courses,” she says. q

SouthAfricahasanunemploymentrateof approximately27%, yet at the same time, the country has a shortage of suitably qualified people. To help address this anomaly, Engen offers a variety of learnerships in technical

Engen aligns its learnership recruitment to its business needs. Those who complete their learnerships are often employedby the

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