In this paper, a novel automatic generation control for a multi-area power system is developed. The proposed controller is based on the Immune-Reinforcement-Learning Algorithm and is capable of real-time adjustment to comply with North American Electric Reliability Council (NERC) control performance standards as well as decreasing the generators’ oscillations. High-renewable penetration, such as wind power, reduces the total inertia of the system which makes the system unable to recover from perturbation causing deterioration in the control performance standards. Because of the intermittency of the renewables, constant gain controllers do not perform well and tend to violate the NERC standards. The proposed algorithm acts in real time and adapts with changes in the renewable power as well as changes in the load to always comply with the standards while smoothly running the generators to reduce wear and tear. The proposed controller is implemented on a four-area power system and compared to different fixed value cases.
Electrical Engineering (Archiv fur Elektrotechnik) – Springer Journals
Published: Oct 21, 2016
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera