• CANFIS based DSTATCOM modelling for solving power quality problems (2021-03-18)
    Bala Boyi Bukata Bayero University Kano R. A. Gezawa Kano Electricity Distribution Company, KEDCO, Nigeria

    Devolution of the power grid into smart grid was necessitated by the proliferation of sensitive load profiles into the system, as well as incessant environmental challenges. These two factors culminated into aggravated disturbances that cause serious havoc along the entire system structure. The traditional proportional-plus-integral-plus-derivative (PID) solution offered by the distribution synchronous compensator (DSTATCOM) could no longer hold. As such, this paper proposes some soft-computing framework for redesigning DSTATCOM to automatically deal with power quality (PQ) problems in smart distribution grids. A recipe of artificial neural network (ANN) and coactive neuro-fuzzy inference systems (CANFIS) was fabricated for the objective. The system was modelled, simulated, and validated in MATLAB/Simulink SimPowerSystems environment. The performance of the CANFIS against adaptive neuro-fuzzy inference systems (ANFIS), ANN and fuzzy logic controllers’ algorithms proved superior in handling PQ issues like voltage sag, voltage swell and harmonics.

  • Prediction of heat input: A TIG process parameter needed to eliminate post weld crack formation and stabilize heat input in mild steel weldment (2021-04-08)
    P. Pondi University of Benin J. Achebo University of Benin A. Ozigagun University of Benin

    The focus of this study is to predict tungsten inert gas (TIG) welding process parameter such as heat input for eliminating post weld crack formation, and stabilizing heat formation in mild steel weldment. The key input parameters considered are welding current, welding voltage and welding speed while the response or measured parameter is heat input. Using the range and levels of the independent variables, statistical design of experiment using central composite design method was done. Hundred (100) pieces of mild steel coupons measuring 60 mm x 40 mm x 10 mm were used for the experiments. The experiment was performed 20 times, using 5 specimens for each run. The plate samples were 60 mm long with a wall thickness of 10 mm. The samples were cut longitudinally with a Single-V joint preparation. The TIG welding equipment was used to weld the plates after the edges have been beveled and machined. The welding process uses 100% pure Argon as shielding gas to protect the weld specimen from atmospheric interaction and the interaction between the input and response variables was analyzed using a fuzzy logic system. From the result, it was observed that for a current of 190 A, voltage of 21 V, and welding speed of 2.0 mm/s, the predicted heat input was 0.912 kJ/mm and for a current of 170 A, voltage of 25 V, and welding speed of 2.0 mm/s, the predicted heat input was 1.07 kJ/mm while for a current of 180 A, voltage of 23 V, and welding speed of 0.98 mm/s, the predicted heat input was 1.380 kJ/mm.