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This paper presents an investigative work about the application of State Space Model-Based Predictive Control in a three-phase Permanent Magnet Synchronous Motor with trapezoidal back-electromotive force, for speed control. Such motor is utilized in the white goods appliances industry and also in automotive and medical applications, among others, especially due to its high effiency and long life cycle. The predictive control methods present a differential in the driving performance for industrial applications, mainly by enabling the imposition of constraints. In this work, a linear prediction model identified with a Least Mean Squares algorithm is used with the State-Space predictive control approach. Such predictive method is interesting for industrial applications for being easy to tune and, in addition to the imposition of constraints, allowing ponderation between tracking performance and spent energy. The utilization of constraints is discussed for the predictive algorithm in this work. There are satisfactory simulated and experimental results that show advantages in using the mentioned control method to drive the Permanent Magnet Synchronous Motor.

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.

-This paper presents a predictive control approach for speed control of a permanent magnet synchronous motor with trapezoidal back-electromotive force drive.The prediction model was numerically identified and considers existent transport delays in the drive. The proposed technique operates with sixstep and pulse-width modulations, which are normally used in proportional-integrative control structures. A computational cost analysis was also done. Results show improvements in speed performance, comparing to tested proportional-integral control.

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Summary This paper presents a nonlinear control approach for 3‐phase induction motors. The proposed structure combines a 3‐phase predictive controller with an integrative reference filter. The predictive controller is designed based on an induction motor model established in natural variables (without using transformations), which is a nonlinear and time‐variant one. This model enables the controller to work independently with the supply voltages, considering unbalanced situations. A dynamic evaluation of the state equation coefficients is used to perform the process variables prediction, thereby executing a point‐to‐point linearization. The conversion of the rotation speed and stator flux modulus reference values is realized by a integrative 3‐phase referrer, which acts as a reference filter, expressing the references as 3‐phase signals and acting as an integrator to eliminate steady‐state errors. Also, a constraint feature is implemented, to reduce the currents. Simulation results satisfactorily show the proposed control architecture characteristics for various reference values and for motor operation as a brake and with load variation.