Abstract By combining various renewable energy sources, new hybrid power generation technologies can significantly improve energy utilization efficiency and reduce environmental impact. However, due to the uncertainty and intermittent nature of this technology, signal optimization and control become particularly important. In this study, we use advanced techniques to enhance the performance of hybrid power generation systems. Our work involves utilizing deep learning to analyze and... By combining various renewable energy sources, new hybrid power generation technologies can significantly improve energy utilization efficiency and reduce environmental impact. However, due to the uncertainty and intermittent nature of this technology, signal optimization and control become particularly important. In this study, we use advanced techniques to enhance the performance of hybrid power generation systems. Our work involves utilizing deep learning to analyze and predict various factors, thereby finding the optimal operating mode. The conclusions of this work will have a positive impact on future energy management and dispatching. Additionally, the methods proposed in this article can provide valuable references for research in other related fields. Read More Read Less

Abstract This paper proposes a method for identifying partial discharge types based on multi fractaldetrended fluc- tuation analysis. This method transforms partial discharge signals through multi fractal transformation, extracts the fractal features of the signals, and combines with detrended fluctuation analysis to accurately identify partial discharge types. Advanced algorithms and techniques are used in this research to classify and analyze different types of partial discharges,... This paper proposes a method for identifying partial discharge types based on multi fractaldetrended fluc- tuation analysis. This method transforms partial discharge signals through multi fractal transformation, extracts the fractal features of the signals, and combines with detrended fluctuation analysis to accurately identify partial discharge types. Advanced algorithms and techniques are used in this research to classify and analyze different types of partial discharges, achieving significant results. Experimental results demonstrate that this method exhibits high accuracy and reliability in identifying partial discharge types, providing a new effective means for partial discharge monitoring and fault diagnosis. Read More Read Less

Abstract A packing k-coloring for a graph G = (V, E) is defined as a function that assigns colors from the set {1, ..., k} to the vertices in V. This assignment must ensure that any two vertices u and v that share the same color c are separated by a distance greater than c within the graph G. One of the key... A packing k-coloring for a graph G = (V, E) is defined as a function that assigns colors from the set {1, ..., k} to the vertices in V. This assignment must ensure that any two vertices u and v that share the same color c are separated by a distance greater than c within the graph G. One of the key challenges in the area of packing colorings is to ascertain the packing chromatic number of the infinite square grid. Prior research has estab- lished that this number lies between 13 and 15. Our study enhances the lower limit to 14. Additionally, we introduce a novel encoding method that offers asymptotically greater compactness compared to those previously employed. Read More Read Less