PERFORMANCE ASSESSMENT OF CONTROL LOOPS: controller evaluation for frequent set-point changes Article Swipe
The high seasonality and variability of electricity production from renewable sources has enormous \nimpact on both production and distribution networks. Among the many aspects, several traditional \npower plants, especially, combined cycle plants, but also coal-fired plants, now operate \nintermittently with variable set points due to fluctuations of energy load which they are requested to \ndeliver [1]. Thus, a specific analysis of control system performance during these transient cyclic \nphases, as reference changes, operations of start up and shutdown, is highly desirable. \nGenerally speaking, monitoring and assessment of performance of control systems of industrial \nplants are important topics in process control. The deterioration in performance is, in fact, a fairly \ncommon phenomenon and manifests with sluggish or oscillating trends of control variables. \nOscillations in control loops can cause many problems which affect normal operation of process \nplants. Typically, fluctuations increase variability of product quality, accelerate wear of equipment, \nmove operating conditions away from optimality, and, in general, cause excessive or unnecessary \nconsumption of energy and raw materials [2]. \nThis paper introduces a technique for the analysis of performance of basic control loops when \nprocess is subject to changes of operating conditions. The method employs the well-established \napproach of Internal Model Control, IMC. After establishing lower limit for the absolute value of \nthe integral (IAE) of control error and the total variation (TV) of control action, such limits are \nassumed as reference values for a control considered “optimal”, or anyway “good”. A performance \nindex is thus based on IMC and is properly defined with respect to lower limit of IAE and TV. With \nthis approach, the validity of tuning of PID-type controller in response to any reference change can \nbe assessed. In particular, one can successfully evaluate closed-loop performance for setpoint \nchanges, as steps, ramps, or generic trends, as for the common case of preset programs of variable \nload of power plants.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://hdl.handle.net/11568/904108
- http://www.mcmcongressi.it/wp-content/uploads/2016/06/Programma-aggiornato-GRICU.pdf
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2786775752
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2786775752Canonical identifier for this work in OpenAlex
- Title
-
PERFORMANCE ASSESSMENT OF CONTROL LOOPS: controller evaluation for frequent set-point changesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-01-01Full publication date if available
- Authors
-
Riccardo Bacci di Capaci, Claudio ScaliList of authors in order
- Landing page
-
https://hdl.handle.net/11568/904108Publisher landing page
- PDF URL
-
https://www.mcmcongressi.it/wp-content/uploads/2016/06/Programma-aggiornato-GRICU.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.mcmcongressi.it/wp-content/uploads/2016/06/Programma-aggiornato-GRICU.pdfDirect OA link when available
- Concepts
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Set point, Controller (irrigation), Set (abstract data type), Computer science, Control (management), Control theory (sociology), Artificial intelligence, Engineering, Control engineering, Biology, Programming language, AgronomyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of \nthe | 194 |
| abstract_inverted_index.successfully | 263 |
| abstract_inverted_index.deterioration | 94 |
| abstract_inverted_index.“optimal”, | 218 |
| abstract_inverted_index.With \nthis | 242 |
| abstract_inverted_index.[2]. \nThis | 154 |
| abstract_inverted_index.to \ndeliver | 50 |
| abstract_inverted_index.are \nassumed | 210 |
| abstract_inverted_index.when \nprocess | 168 |
| abstract_inverted_index.fairly \ncommon | 101 |
| abstract_inverted_index.variable \nload | 284 |
| abstract_inverted_index.cyclic \nphases, | 63 |
| abstract_inverted_index.enormous \nimpact | 12 |
| abstract_inverted_index.equipment, \nmove | 136 |
| abstract_inverted_index.process \nplants. | 125 |
| abstract_inverted_index.industrial \nplants | 86 |
| abstract_inverted_index.performance \nindex | 223 |
| abstract_inverted_index.setpoint \nchanges, | 268 |
| abstract_inverted_index.traditional \npower | 24 |
| abstract_inverted_index.desirable. \nGenerally | 75 |
| abstract_inverted_index.operate \nintermittently | 35 |
| abstract_inverted_index.unnecessary \nconsumption | 148 |
| abstract_inverted_index.variables. \nOscillations | 112 |
| abstract_inverted_index.well-established \napproach | 180 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.2835252 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |