Electrical Machine Condition Monitoring at Spanish Paper Plant

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Electrical Machine Condition Monitoring at Spanish Paper Plant 2014-09-02T08:32:32+00:00

Project Description

For the most part, generators are very efficient, and are typically quite reliable. They do need attention from time to time, however, to prevent disastrous failures that could end up costing millions of Euro’s, and causing long wait times for replacement parts.

Before these units can be properly reconditioned, however, their existing condition needs to be diagnosed correctly. An extensive generator health assessment should be done to determine what problems are most severe and need priority attention. Running to failure can result in additional damage above and beyond the reconditioning cost, so it is critical to identify problems as early as possible and fix them before they cause extended damage.

Our customer,  a Spanish pulp & paper plant, asked us to determine the condition of one of it’s main generators. The particular generator was a Siemens generator with an output power of 25 MVA at a rated voltage of 10,5 kV.

Our engineers carried out a complete Electrical Machine Condition Monitoring test on site. Various tests were conducted like; Partial Discharge, Tan Delta, Polarization Index, surge comparison, Low Resistance measurements etc. etc. These measurements combined with information from the borescope video inspection and mechanical measurements of critical generator parts, SPIT estimated the remaining ‚trouble free’ operational periode for this generator.

Main advantages for our customer:

  • Optimizes Maintenance Planning of Electrical Machines by moving from Time Based Maintenance to Condition Based Maintenance.

  • Provides an opportunity for Life Extension of machines, which would lead to increased earnings capability and greater Return on Investment.
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Facilitates decision making: A single occasion of measurements is sufficient to enable short and long term maintenance plans, repair-run-replace decisions.
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Focuses on essential maintenance, and machines that are vulnerable, to reduce downtime at lower risk levels

  • Provides important “lifetime ” inputs for more realistic estimates of Life Cycle Costs