{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T11:59:07Z","timestamp":1783079947620,"version":"3.54.6"},"reference-count":35,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,7,1]]},"abstract":"<p>The main objective of Load Frequency Control (LFC) is to regulate the power output of the electric generator within an area in response to changes in system frequency and tie-line loading. Thus the LFC helps in maintaining the scheduled system frequency and tie-line power interchange with the other areas within the prescribed limits. Most LFCs are primarily composed of an integral controller. The integrator gain is set to a level that compromises between fast transient recovery and low overshoot in the dynamic response of the overall system. This type of controller is slow and does not allow the controller designer to take into account possible changes in operating conditions and non-linearities in the generator unit. Moreover, it lacks robustness. This paper studies LFC in two areas power system using PID controller. In this paper, PID parameters are tuned using different tuning techniques. The overshoots and settling times with the proposed controllers are better than the outputs of the conventional PID controllers. This paper uses MATLAB\/SIMULINK software. Simulations are done by using the same PID parameters for the two different areas because it gives a better performance for the system frequency response than the case of using two different sets of PID parameters for the two areas. The used methods in this paper are: a) Particle Swarm Optimization, b) Adaptive Weight Particle Swarm Optimization, c) Adaptive Acceleration Coefficients based PSO (AACPSO) and d) Adaptive Neuro Fuzzy Inference System (ANFIS). The comparison has been carried out for these different controllers for two areas power system. Therefore, the article presents advanced techniques for Load Frequency Control. These proposed techniques are based on Artificial Intelligence. It gives promising results.<\/p>","DOI":"10.4018\/ijsda.2014070101","type":"journal-article","created":{"date-parts":[[2014,10,29]],"date-time":"2014-10-29T12:45:40Z","timestamp":1414586740000},"page":"1-24","source":"Crossref","is-referenced-by-count":16,"title":["Load Frequency Control in Power System via Improving PID Controller Based on Particle Swarm Optimization and ANFIS Techniques"],"prefix":"10.4018","volume":"3","author":[{"given":"Naglaa K.","family":"Bahgaat","sequence":"first","affiliation":[{"name":"Elec. Comm. Dept. Faculty of Engineering, Canadian International College (CIC), 6 October City, Giza, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. I.","family":"El-Sayed","sequence":"additional","affiliation":[{"name":"Electrical Power Engineering Dept. Faculty of Engineering, Al-Azhar University, Cairo, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. A. Moustafa","family":"Hassan","sequence":"additional","affiliation":[{"name":"Electrical Power Engineering Dept. Faculty of Engineering, Cairo University, Giza, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F. A.","family":"Bendary","sequence":"additional","affiliation":[{"name":"Electrical Power Engineering Dept. Faculty of Engineering, Banha University, Cairo, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijsda.2014070101-0","doi-asserted-by":"publisher","DOI":"10.1109\/CoDIT.2013.6689546"},{"key":"ijsda.2014070101-1","doi-asserted-by":"publisher","DOI":"10.1504\/IJMIC.2012.051085"},{"issue":"3","key":"ijsda.2014070101-2","first-page":"1489","article-title":"Nonconvex economic dispatch with AC constraints by a new real coded genetic algorithm. Power Systems","volume":"24","author":"N.Amjady","year":"2009","journal-title":"IEEE Transactions on"},{"key":"ijsda.2014070101-3","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2005.847331"},{"key":"ijsda.2014070101-4","author":"A. T.Azar","year":"2010","journal-title":"Fuzzy Systems"},{"key":"ijsda.2014070101-5","doi-asserted-by":"publisher","DOI":"10.5772\/7220"},{"key":"ijsda.2014070101-6","doi-asserted-by":"publisher","DOI":"10.4018\/ijfsa.2012100101"},{"key":"ijsda.2014070101-7","doi-asserted-by":"publisher","DOI":"10.4018\/ijsda.2012040104"},{"key":"ijsda.2014070101-8","doi-asserted-by":"publisher","DOI":"10.1080\/15325008.2012.689418"},{"key":"ijsda.2014070101-9","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84878-5"},{"key":"ijsda.2014070101-10","author":"R.Bishop","year":"2000","journal-title":"Modern Control Systems"},{"key":"ijsda.2014070101-11","unstructured":"Chaudhari, O. K., Khot, P. G., Deshmukh, K. C., & Bawne, N. G. (2012). Anfis Based Model in Decision Making to Optimize the Profit in Farm Cultivation. International Journal of Engineering Science, 4)2), 442-448."},{"issue":"2","key":"ijsda.2014070101-12","first-page":"384","article-title":"A Particle Swarm Optimization approach for optimum design of PID controller in AVR system. Energy Conversion","volume":"19","author":"Z. L.Gaing","year":"2004","journal-title":"IEEE Transactions on"},{"key":"ijsda.2014070101-13","doi-asserted-by":"crossref","unstructured":"Ghomsheh, V. S., Shoorehdeli, M. A., & Teshnehlab, M. (2007, June). Training ANFIS structure with modified PSO algorithm. In Control & Automation, 2007. MED'07. Mediterranean Conference on (pp. 1-6). IEEE.","DOI":"10.1109\/MED.2007.4433927"},{"key":"ijsda.2014070101-14","doi-asserted-by":"publisher","DOI":"10.1109\/UKSIM.2010.82"},{"key":"ijsda.2014070101-15","unstructured":"Ismail, A. (2006, April). Improving UAE power systems control performance by using combined LFC and AVR. In The Seventh UAE University Research Conference, ENG (pp. 50-60)."},{"key":"ijsda.2014070101-16","doi-asserted-by":"publisher","DOI":"10.4018\/ijsda.2012100106"},{"key":"ijsda.2014070101-17","unstructured":"Ismail, M. M., & Hassan, M. M. (2012, January). Load Frequency Control Adaptation Using Artificial Intelligent Techniques for One and Two Different Areas Power System. International Journal of Control Automation and Systems. 1)1(, 12-23."},{"key":"ijsda.2014070101-18","doi-asserted-by":"publisher","DOI":"10.1504\/IJMIC.2012.047119"},{"key":"ijsda.2014070101-19","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.1998.798284"},{"issue":"11","key":"ijsda.2014070101-20","first-page":"6","article-title":"Hybrid Learning For Adaptive Neuro Fuzzy Inference System.","volume":"2","author":"C.Loganathan","year":"2013","journal-title":"International Journal of Engineering Science"},{"issue":"31","key":"ijsda.2014070101-21","first-page":"6475","article-title":"A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems.","volume":"6","author":"O. F.Lutfy","year":"2011","journal-title":"Scientific Research and Essays"},{"key":"ijsda.2014070101-22","unstructured":"Naik, R. S., ChandraSekhar, K., & Vaisakh, K. (2005). Adaptive PSO based optimal fuzzy controller design for AGC equipped with SMES and SPSS. Journal of Theoretical and Applied Information Technology, 7(1), 008-017."},{"key":"ijsda.2014070101-23","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2007.12.023"},{"issue":"5","key":"ijsda.2014070101-24","first-page":"1311","article-title":"PSO Based Design of Robust Controller for Two Area Load Frequency Control with Nonlinearities.","volume":"2","year":"2010","journal-title":"International Journal of Engineering Science"},{"key":"ijsda.2014070101-25","unstructured":"Rania, H. Mansour (2012). Development of advanced controllers using adaptive weighted PSO algorithm with applications, M. Sc Thesis, Faculty of Engineering, Cairo University, Cairo, Egypt."},{"key":"ijsda.2014070101-26","doi-asserted-by":"publisher","DOI":"10.1109\/PECON.2006.346651"},{"key":"ijsda.2014070101-27","doi-asserted-by":"publisher","DOI":"10.1016\/S0959-1524(02)00062-8"},{"key":"ijsda.2014070101-28","doi-asserted-by":"publisher","DOI":"10.1049\/pe:19970505"},{"key":"ijsda.2014070101-29","doi-asserted-by":"publisher","DOI":"10.1049\/pe:19980403"},{"key":"ijsda.2014070101-30","unstructured":"Tammam, M. A. (2011). Multi objective genetic algorithm controllers Tuning for load frequency control in Electric power systems. Cairo, Egypt: M. Sc. Thesis, Faculty of Engineering at Cairo University."},{"key":"ijsda.2014070101-31","unstructured":"Tammam, M. A., Aboelela, M. A. S., Moustafa, M. A., & Seif, A. E. A. (2012a, June). Load Frequency Controller Design for Interconnected Electric Power System. 55th Annual Power Industry division Symposium POWID 2012, Austin, Texas, USA."},{"issue":"1","key":"ijsda.2014070101-32","first-page":"572","article-title":"Fuzzy Like Pid Controller Tuning by Multi-Objective Genetic Algorithm for Load Frequency Control in Nonlinear Electric Power Systems.","volume":"5","author":"M. A.Tammam","year":"2012","journal-title":"International Journal of Advances in Engineering & Technology"},{"issue":"2","key":"ijsda.2014070101-33","first-page":"991","article-title":"Economic load dispatch\u2014A comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO. Power Systems","volume":"24","author":"J. G.Vlachogiannis","year":"2009","journal-title":"IEEE Transactions on"},{"key":"ijsda.2014070101-34","doi-asserted-by":"publisher","DOI":"10.1049\/ip-c.1993.0003"}],"container-title":["International Journal of System Dynamics Applications"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=117672","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T16:02:12Z","timestamp":1746460932000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/ijsda.2014070101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2014,7,1]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2014,7]]}},"URL":"https:\/\/doi.org\/10.4018\/ijsda.2014070101","relation":{},"ISSN":["2160-9772","2160-9799"],"issn-type":[{"value":"2160-9772","type":"print"},{"value":"2160-9799","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,7,1]]}}}