The use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. In this paper, convolutional neural networks (CNN) are used to extract features of the time series, and long short-term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.02562 shows that the CNN-LSTM is a promising machine learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real-world applications. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

Faults in power systems occur for various reasons, such as aging or natural disasters. Detecting, locating, and promptly clearing these faults is crucial for maintaining the safety and reliability of transmission lines. Distance relays, which protect transmission lines, detect faults, estimate their location, and send the required commands. However, these relays may experience mis-detection due to manipulated impedance arising from both internal and external factors. These factors include measurement device errors, network topology changes, the presence of fault resistance, and injected currents from remote line terminals. To address this challenge, we propose an innovative adaptive protection scheme that considers fault resistance, changes in network topology, and injected current from the opposite end of the line. By estimating the equivalent circuit impedances of the network connected to the terminal of the transmission line, this protection scheme utilizes impedance estimation techniques at the line terminals and offline network information. Simulation studies (tested on the IEEE 39-bus standard network) show that the proposed scheme accurately estimates fault location and fault resistance with high precision. The simulation results demonstrate its effectiveness in improving the performance of conventional distance protection relays in both the first and second protection zones (Z_1 and Z_2).

This brief investigates the multi-point fault repair problem in active distribution networks, establishing resilience assessment metrics and constructing a fault repair mathematical model with road network coupling. An improved genetic algorithm is proposed, utilizing a comparison crossover operator and population information entropy. Compared to traditional algorithms, the comparison crossover operator preserves high-quality genes, while population information entropy maintains diversity, preventing the algorithm from converging on local optima. Simulation analysis demonstrates that the proposed fault repair method can restore normal power supply to distribution network lines within a short period and offers significant advantages in fault repair tasks.