Adaptive neuro-fuzzy controller based on simplified ANFIS network

Albakkar, A.; Malik, O.P.

doi:10.1109/pesgm.2012.6344842

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The three-dimensional MF is the basis for the difference in the structure of FL-T2 from FL-T1 in the presence of the reducer type. MF in FL-T2 has an upper bound for the membership value and a lower bound, which are diffusion in the interval (0, 1) so that (MF) in FL-T2 has a footprint of uncertainty (FOU) [20], [22], [23]. MATLAB 2021 is utilized to simulate and operate FL-T2.…”

Section: Type-1 Fuzzy Logic and Type-2 Controlmentioning

confidence: 99%

Controlling the effectiveness of STATCOM using ANFIS based on PI controller

2023

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This research achieves two goals, the first is the modelling of the static synchronous compensator (STATCOM) and design of the control circuits for current, voltage and d.c capacitor voltage (Vd.c) to improve the power factor (PF) by preparing the reactive power by STATCOM so that its work is similar to the work of a synchronous condenser, and the second goal is to use smart techniques to control the Vd.c loop. For comparison, smart technical methods such as fuzzy logic type 1 (FL-T1), fuzzy logic type 2 (FL-T2), and adaptive neuro fuzzy inference system (ANFIS) were used to regulate the Vd.c on the capacitor instead of the traditional controller proportional integral (PI). Simulation was performed in MATLAB 2021 to determine the efficiency of the suggested method or approach controllers for STATCOM. The use of the proposed method improves the signal value of both the current and the voltage and the phase difference between them, which reaches almost zero. Proved that an ANFIS technique provides best Vd.c response in different values of balance and unbalance load compared with the other methods by obtaining the minimum peak overshoot (P-ov) and minimum settling time (ST).

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Section: Type-1 Fuzzy Logic and Type-2 Controlmentioning

confidence: 99%

Controlling the effectiveness of STATCOM using ANFIS based on PI controller

2023

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“…These input variables may be the state error, derivative of state error or integral of state error, etc. In the power system; the generator speeds deviation (Dx) and acceleration (D _ x), were chosen as the input signals of fuzzy PSS [36,37]. In practise, the shaft deviation (Dx) of the generator is readily available [21,25].…”

Section: Fuzzy Logic Processmentioning

confidence: 99%

“…5 can be obtained using machine data, the line data, and load flow [24,26], and are given as following in Eqs. 36,37.…”

Section: State Space Modelmentioning

confidence: 99%