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<Article>
<Journal>
<PublisherName>OICC Press</PublisherName>
<JournalTitle>International Journal of Energy and Environmental Engineering</JournalTitle>
<Issn>2251-6832</Issn>
<Volume>14</Volume>
<Issue>4 (December 2023)</Issue>
<PubDate PubStatus="epublish">
<Year>2022</Year>
<Month>11</Month>
<Day>03</Day>
</PubDate>
</Journal>
<ArticleTitle>Influence of solar and solid oxide fuel cell in AGC learning by utilizing spotted hyena optimized cascade controller</ArticleTitle>
<VernacularTitle></VernacularTitle>
<FirstPage></FirstPage>
<LastPage></LastPage>
<ELocationID EIdType="doi">10.1007/s40095-022-00547-9</ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>Arindita</FirstName>
<LastName>Saha</LastName>
<Affiliation>Department of Electrical Engineering, Regent Education &amp; Research Foundation Group of Institutions, Kolkata, West Bengal, IN</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>Puja</FirstName>
<LastName>Dash</LastName>
<Affiliation>Department of Electrical and Electronics Engineering, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam, Andhra Pradesh, IN</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>Naladi Ram</FirstName>
<LastName>Babu</LastName>
<Affiliation>Department of Electrical and Electronics Engineering, Aditya Engineering College, East-Godavari, Andhra Pradesh, IN</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
</AuthorList>
<PublicationType>Journal Article</PublicationType>
<History>
<PubDate PubStatus="received">
<Year>2022</Year>
<Month>11</Month>
<Day>03</Day>
</PubDate>
</History>
<Abstract>Abstract
The present work explores automatic generation control learning for manifold area and sources under traditional situations. Sources in area-1 are thermal, biodiesel; thermal and gas plant in area-2; and thermal, split-shaft gas turbine (Ss(GT)) in area-3. An original strive has been set out to execute cascade controller with the amalgamation of proportional with tilt–integral–derivative with filter (TIDN) and fractional-order integral–derivative (FOID). TIDN and FOID are in series connection, hence named TIDN-FOID. Various scrutiny expresses excellency of TIDN-FOID controller over proportional–integral–derivative filter (PIDN) and TIDN from outlook regarding the lessened level of peak overshoot, extent of oscillations, peak undershoot as well as settling time. In an endeavour to procure the controller’s gains and parameters, bioinspired meta-heuristic spotted hyena optimizer (SHO) is applied. It is also observed that the presence of a renewable solar source makes the system significantly better compared to the base thermal–biodiesel–gas–Ss(GT) system. TIDN-FOID performance is also observed to be excellent in the presence of solar for both 1% step load disturbance and random load pattern individually. Fixed as well as variable insolation for solar is analysed separately. The performance of solid oxide fuel cell (SoFC) is also examined using the TIDN-FOID controller, which provides with noteworthy outcome in dynamic performance for both types of disturbances. Also, sensitivity analysis is performed, and it is observed that the values of the TIDN-FOID parameters at nominal conditions are appropriate for a higher value of disturbance.</Abstract>
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<Object Type="keyword">
<Param Name="value">Automatic generation control</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Biodiesel plant</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Solar thermal power plant</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Solid oxide fuel cell</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Spotted hyena optimizer</Param>
</Object>
<Object Type="keyword">
<Param Name="value">TIDN-FOID controller</Param>
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</Article>
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