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This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor's speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed. [ABSTRACT FROM AUTHOR]

Polymer flooding is a prominent enhanced oil recovery process that is widely recognized for its cost-effectiveness and substantial success in increasing oil production. In this study, the Buckley–Leverett mathematical model for polymer flooding was used to generate more than 163,000 samples that reflect different reservoir conditions using seven input parameters. We introduced artificial noise into the dataset to simulate real-world conditions and mitigate overfitting. Seven classic machine learning models and two neural networks were trained on this dataset to predict the oil recovery factor based on the input parameters. Among these, polynomial regression performed best with a coefficient of determination ( R 2 ) of 0.909, and the dense neural network and cascade-forward neural network achieved R 2 scores of 0.908 and 0.906, respectively. Our analysis included permutation feature importance and metrics analysis, where key features across all models were identified, and the model's performance was evaluated on a range of metrics. Compared with similar studies, this research uses a significantly larger and more realistic synthetic dataset that explores a broader spectrum of machine learning models. Thus, when applied to a real dataset, our methodology can aid in decision-making by identifying key parameters that enhance oil production and predicting the oil recovery factor given specific parameter values. [ABSTRACT FROM AUTHOR]