An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods.

This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. Traditional PID controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent in interconnected power systems, leading to slower settling time and higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates these issues by utilizing LSTM's capacity to learn from historical data by using gradient descent to forecast the future disturbances, while the GA optimizes the PID parameters in real time, ensuring dynamic adaptability and improved control precision.

Thermotolerance screening of genotypes using seed germination assay.

Temperature is a fundamental factor influencing the processes of seed germination. Investigating the response of carinata to thermal stress and establishing a dependable and efficient method for screening thermotolerance will enhance breeding programs and model applications. We assessed the response of 12 carinata genotypes to a range of eight temperatures, spanning from 8 to 37 °C, throughout the germination process.