Reference projects

AREAOperation and maintenance

Realization of projects related to operation and maintenance of power plants in order to track operational parameters and manage operational and maintenance costs. Connection of power sources into virtual dispatches. Analysis of equipment registry (KKS code). Preparation of environment for monitoring of the operational and costs indicators (Power BI).

Analysis of CHP Skawina auxiliary consumption (ČEZ, a.s.)

• Preparation of analysis and solution for auxiliary consumption optimisation
• Proposal of auxiliary consumption methodology evaluation
• Calculation of required operational options
• Negotiation with client, presentation of analysis results

Analysis of central dispacht creation (E.ON Trend, s.r.o.)

• Analysis of sources structure and technical features
• Proposal of technical and business model
• Economic analysis
• Proposal on internal processes change

Analysis of environment for virtual unit creation (Tedom)

• Analysis of sources structure and technical features
• Analysis of legal Framework incl. ancillary services
• Framework schedule of preparation and realization
• Economic evaluation

Analysis of power plant cost characteristics, creation of maintenance dashboard (ČEZ, a.s.)

• Proposal of project solution concept, work management
• Analysis of maintenance materials supplied externally (quantity, costs)
• Analysis of AS8 functionality utilisation related to maintenance works (technical parameters, bill of materials, link of equipment and project positions, etc. and recommendation of data consolidation procedure
• Analysis of links in equipment registry to identify individual machines within same locality and across different localities
• Creation of the Dashboard for reporting of chosen data items connected to work management in the MS Power BI system (work orders, materials, workload, costs, etc.)

Early Warning system

• Proposal of the solution concept, management of expert analytical team, subject matter expert
• Offline analysis of measured data of given machine before event, during event and  after event using statistical methods
• Proposal of the detect algorithms of failures based on offline analysis results
• Testing of detection algorithms