Abstract
Selective harmonic elimination pulse-width modulation (SHEPWM) is a widely adopted method to eliminate harmonics in multilevel inverters, yet solving harmonic amplitude equations is both time consuming and not accurate. This method is applied here for a 7-level cascaded multilevel inverter (CMLI) with erroneous DC sources. To meet the seven harmonic amplitude equations, two notches are applied with the use of higher switching frequency than nominal. These notches can be placed in six different positions in the voltage wave, and each was assessed in a separate manner. In order to solve the equations, a hybrid algorithm composed of genetic algorithm (GA) and Newton–Raphson (N-R) algorithm is applied to achieve faster convergence and maintain the accuracy of stochastic methods. At each step of the modulation index (M), different positions for the notches are compared based on the distortion factor (DF2%) benchmark, and the position with lowest DF2% is selected to train an artificial neural fuzzy interface system (ANFIS). ANFIS will receive the DC sources’ voltages together with required M and will produce one output; thus, eight ANFISs are applied to produce seven firing angles, and the remaining one is to determine which one of the notches’ positions should be used. Software simulations and experimental results confirm the validity of this proposed method. The proposed method achieves THD 8.45% when M is equal to 0.8 and is capable of effectively eliminating all harmonics up to the 19th order.
Highlights
As the power and voltage demands are getting higher and with the wide consumption rate of low-voltage DC sources such as the fuel cell and photovoltaic arrays [1], the need for multilevel inverters (MLIs) that receive these low voltages and produce high or medium voltage levels becomes obvious
A hybrid algorithm composed of genetic algorithm (GA) and N-R algorithms is proposed to determine the best firing angles for each one of the assessed situations. e angles with lowest distortion factor (DF2%) in each step of M in different notches’ positions are picked and are applied to train an adaptive neural fuzzy interface system (ANFIS) and to determine the angles in real time. e performance of the proposed system is exhibited through simulation and experimental results
For M near zero, the equations are not solvable, and the first harmonic amplitude is going to be less than the 5th- and the 7th-order amplitudes, an undesirable phenomenon
Summary
As the power and voltage demands are getting higher and with the wide consumption rate of low-voltage DC sources such as the fuel cell and photovoltaic arrays [1], the need for multilevel inverters (MLIs) that receive these low voltages and produce high or medium voltage levels becomes obvious. Because the objective of applying MLIs is to reduce the switching losses, too many notches can increase the frequency and switching losses. In other studies, such as [18], the higher harmonic amplitudes are neglected, and fewer equations than unknowns are solved in order to find better solutions for the remaining equations. Two notches are added in different positions to the output voltage wave in every M step value, and the best position is selected to be applied online; under load, a change in M can change the notches’ positions from one voltage level to another to reach minimum harmonics. A hybrid algorithm composed of GA and N-R algorithms is proposed to determine the best firing angles for each one of the assessed situations. e angles with lowest distortion factor (DF2%) in each step of M in different notches’ positions are picked and are applied to train an adaptive neural fuzzy interface system (ANFIS) and to determine the angles in real time. e performance of the proposed system is exhibited through simulation and experimental results
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