Page 300 - Sustainable On-Site CHP Systems Design, Construction, and Operations
P. 300

Sustaining Operational Ef ficiency of a CHP System     273


             faults and degradation of the overall system performance. Equipment-level diagnostics
             from different OEMs may need to be integrated to achieve system-level diagnostics.
                Supervisory control and automated diagnostic algorithms can be the basis for auto-
             mated tools that help the building engineer, building manager, or energy service
             provider better manage complex CHP systems and their interactions with existing
             building systems. Three major functional requirements for such supervisory controls
             and diagnostics are
                  1.  Provide continuous feedback to operators on system performance using easily
                    understood performance metrics
                  2.  Automatically detect, diagnose, and project system and equipment degradation
                    and faults using algorithms for automated fault detection, diagnostics, and
                    prognostics for components and systems
                  3.  Provide support for optimization and load balancing using adaptive predictive
                    controls and automated decision support tools


             Continuous Performance Feedback
             Although providing performance feedback to operators or energy service providers
             managing CHP systems will not guarantee optimal operations, it will provide the
             performance information that will enable operators to recognize anomalous situations
             requiring action. The proactive operator will use this information to notice plant changes,
             investigate them, and make necessary corrections and operational changes.


             Automated Diagnostics and Prognostics
             Automated fault detection and diagnosis (AFDD) is an automatic process by which
             faulty operation, degraded performance, and failed components in a physical system
             are detected, understood, and reported. AFDD tools are based on algorithms that pro-
             cess data to determine whether the source of the data is experiencing a fault. For more
             details on AFDD methods for building systems refer to Katipamula and Brambley
             (2005a and 2005b).
                The AFDD tool may be either passive, analyzing operation of the equipment/
             system as it operates, without altering any of its set points or control outputs, or active,
             automatically initiating changes to produce or simulate operating conditions that cover
             a wider range of conditions that might be experienced for a considerable time under
             normal operation.
                Even if the integrated system is commissioned during installation, this does not
             ensure continued proper operation. Only continuous monitoring of the status of the
             equipment and its performance and correction of faults can ensure continued proper
             operation. AFDD systems are central to this continuous monitoring and commissioning
             process by constantly monitoring equipment and identifying faults or degradation in
             performance. Further, prognostic tools can inform operators and maintenance person-
             nel regarding the time before failure or significant performance degradation, enabling
             personnel to anticipate and plan for maintenance. The human operator or repair person
             is still critical to completing the commissioning and maintenance cycles, but without
             continuous, automated systems monitoring, problems can go undetected for days,
             weeks, months, or even years and none can be anticipated in advance.
   295   296   297   298   299   300   301   302   303   304   305