Summary The modular multilevel converters (MMCs) are frequently preferred for medium and high power energy conversion systems thanks to their modular structure and high quality output waveform. The remarkable studies related to the MMC have been carried out in recent years, which are mainly focused on its topology, control, or application. In this paper, MMC circuit topologies consisting of traditional submodule cells and new proposed configurations are introduced, and their control process, modulation techniques, and application areas are discussed in order to provide the readers the current state of the art of MMC technology. A wide part of this article is devoted to demonstrate the recent contributions arising in MMC studies. In this context, this paper not only outlines the circuit topology, control objectives, and applications of the MMC but also presents a comprehensive review, principally in terms of the latest developments of it. Ultimately, detailed proposals and new possible topics are provided to drive future expectations of MMC technology.

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Summary Balanced operating conditions of photovoltaic (PV)‐connected cascaded H‐bridge multilevel inverters (CHB‐MLIs) require equal number of series and parallel arrays, equal amplitude modulation indices, and same environmental conditions such as temperature and irradiance for each H‐bridge (cell). Unbalanced operating conditions arise when at least one of these requirements are not met and/or a DC fault occurs. Phase‐shifted pulse width modulation (PS‐PWM) is the most widely used technique for CHB‐MLIs. However, under unbalanced operating conditions, harmonics that appear around the effective switching frequency (fsw) of each cell fail to eliminate each other when PS‐PWM is used, and consequently, undesired harmonics are generated at the multiples of fsw on the output voltage of CHB‐MLI. In this paper, a fault tolerant adaptive phase‐shifted pulse width modulation (FTA‐PS‐PWM) method is proposed for any number of cells to overcome this problem. For this reason, a mathematical representation of the harmonics appearing around the multiples of fsw on the output voltage of CHB‐MLI is suggested. In this analysis, on/off instants of the power semiconductors are investigated in frequency domain for a single carrier period. Based on derived formulations, phase angle of each cell is calculated at each sampling time using DC voltages and amplitude modulation indices of all cells in FTA‐PS‐PWM. In order to validate the usefulness of the proposed switching strategy, a three‐phase, PV‐connected CHB‐MLI is established in simulation environment and a prototype of a single‐phase CHB‐MLI is built. It is shown that FTA‐PS‐PWM reduces output voltage harmonics effectively in unbalanced operating conditions including DC faults for different number of cells. Moreover, FTA‐PS‐PWM is also compared with previous methods in the literature and compliance of output harmonics with international standards is examined for various cases. The results reveal that FTA‐PS‐PWM generally provides better results in terms of individual harmonics and total harmonic distortion of output voltages of CHB‐MLI and satisfies the requirements of the standards.

Different optimization techniques are used for the training and fine-tuning of feed forward neural networks, for the estimation of STATCOM voltages and reactive powers. In the first part, the paper presents the voltage regulation in IEEE buses using the Static Compensator (STATIC) and discusses efficient ways to solve the power systems featuring STATCOM by load flow equations. The load flow equations are solved using iterative algorithms such as Newton-Raphson method. In the second part, the paper focuses on the use of estimation techniques based on Artificial Neural Networks as an alternative to the iterative methods. Different training algorithms have been used for training the weights of Artificial Neural Networks; these methods include Back-Propagation, Particle Swarm Optimization, Shuffled Frog Leap Algorithm, and Genetic Algorithm. A performance analysis of each of these methods is done on the IEEE bus data to examine the efficiency of each algorithm. The results show that SFLA outperforms other techniques in training of ANN, seconded by PSO.