000 03982ntm a2200349 i 4500
001 vtls000104492
003 KUKTEM
005 20251117113407.0
008 180725s2018 my da f am 000 0 eng d
020 _aTHE0005181(Local)
039 9 _a201905141434
_bhanafiah
_c201905141433
_dhanafiah
_y201807251126
_zsaini
040 _aUMP
_beng
_cUMP
_erda
090 _aFKEE .H36 2018 r Thesis
100 1 _aHammid, Ali Thaeer,
_eauthor.
245 1 0 _aOptimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques /
_cAli Thaeer Hammid
264 1 _aKuantan, Pahang :
_bUMP,
_c2018
300 _axvii, 172 pages :
_billustrations (some color), charts ;
_c30 cm. +
_e1 CD ROM
336 _atext
_2rdacontent
337 _aunmediated
_2rdamedia
337 _acomputer
_2rdamedia
338 _avolume
_2rdacarrier
338 _acomputer disc
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aFaculty of Electrical and Electronics Engineering
502 _aThesis (Doctor of Philosophy of Engineering in Electrical and Electronics) -- Universiti Malaysia Pahang – 2018
504 _aIncludes bibliographical references
520 3 _aThe fundamental requirement of hydropower system scheduling is to determine the optimal amount of generated powers for the hydro unit of the system in the scheduling horizon of 1 year or few years while satisfying the constraints of the hydroelectric system. Annual hydro generation scheduling (AHGS) is a complicated non-linear, non-convex and non-smooth optimization problem with discontinuous solution space. The model considers daily water inflows, limits on reservoir level, power generation depends on the available head of hydro units caused by power variations, start-up, and shut-down of hydro units. Moreover, hydro generation prediction typically has composite structures such as non-linearity, non-stationarity, and fluctuation due to unexpected variable of input parameters, which converts its prediction to be very tough. Artificial intelligence (AI) methods are normally selected to deal with this problem. However, they are suffering from partial optimization, falling in solutions of local minima, and low speed of convergence. To deal with these problems, this thesis introduces three approved intelligent controllers for hydropower generation. Firstly, a hybrid algorithm namely firefly particle swarm optimization (FPSO) and series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed. In the FPSO method, the local search is performed through the modified light intensity attraction step with PSO operator. Secondly, this approach hybridizing the FA with the rough algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. After that, the stationary simulation prediction model is obtained. Thirdly, a novel normalized firefly fuzzy control method (NFANN) is designed for stability control of a hydro-turbine system. Moreover, the more relaxed and simplified sufficient stability conditions are given as a new set of right-angle triangle membership function (RFANN), which has been guaranteed by strict mathematical derivation. The proposed methods tested on raw data of hydropower plant of Himreen Lake Dam. The optimal hydropower generation that observed is increased to the maximum over the actual value by PSO, SD-PSO, SD-FA, and FPSO increased by 1.5%, 2.3%, 3.1%, 2.5% respectively. The proposed SD-FA controller showed better and robustness compared to the other algorithm, which could resist the random disturbances.
610 2 0 _aFaculty of Electrical and Electronics Engineering
_xDissertations
650 0 _aUniversities and colleges
_xDisertations
650 0 _aTheses
999 _aVIRTUA40
_c7891
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