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This work introduces a 49-level Asymmetrical Inverter (AMLI) with boosted output based on the cascaded operation of two 7-Level Modified Packed U-Cell inverters (MPUC-7). The converter is capable of operation with a boosted voltage of up to 1.714 times the maximum DC voltage employed. It requires only 12 active switches and 4 voltage sources. With the sources set in the ratio of 14 : 7 : 2 : 1, the 7-level output of the two converters is so utilized that the 7 2 = 49-level output voltage is generated across the load. A detailed explanation of level formation is discussed. This converter is operated using an Artificial Neural Network (ANN) which is trained for the harmonic elimination in the output voltage waveform. For the calculation of optimum angles, a meta-heuristic based Genetic Algorithm (GA) technique is employed. The generation of 49-level output requires 24 transitions in one quarter of a cycle. All these angles are generated for various desired output voltages, and the ANN is trained offline for the same. The converter and its control are simulated in MATLAB/Simulink environment, and the results are verified on the experimental setup. The multilevel output thus obtained is nearly sinusoidal and the Total Harmonic Distortion (THD) thus produced is under the specified limit of IEEE.

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Wind speed forecasts can boost the quality of wind energy generation by increasing the efficiency and enhancing the economic viability of this variable renewable resource. This work proposes a hybrid model for wind energy capacity for electrical power generation at coastal sites by utilizing windrelated variables' characteristics. The datasets of three coastal locations of Kuwait validate the proposed method. The hybrid model is a merger of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) and predicts one-month-ahead wind speed for wind power density calculation. The neural network starts its performance evaluation with a variable number of hidden-layer neurons to finally identify the optimal ANN topology. Comparisons of statistical indices with both expected and observed test results indicate that the ANN-PSO based hybrid model with the low root-mean-square-error and meansquare-error values outperforms ANN-based trivial models. The prediction model developed in this work is highly accurate with a Mean Absolute Percentage Error (MAPE) of approximately (3-6%) for all the sites.

This paper illustrates a new application of planar curvature theory to the geometric problem of trajectory generation by a two-link manipulator. The theory yields the instantaneous speed ratio, and the rate of change of the speed ratio, which correspond to the geometry of a desired point trajectory. Separate from the purely geometric speed ratio problem (i.e., the coordination problem) is the time based problem of controlling the joint rates in order to move with the specified path variables.

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