Reactive Power Flow Convergence Adjustment Based on Deep Reinforcement Learning

Power flow calculation is the basis of power grid planning and many system analysis tasks require convergent power flow conditions.To address the unsolvable power flow problem caused by the reactive power imbalance,a method for adjusting reactive power flow convergence based on deep reinforcement learning is proposed.The deep reinforcement learning method takes switching parallel reactive compensation as the action space and sets the reward value based on the power flow convergence and reactive power adjustment.For the non-convergence power flow,the 500 kV nodes with reactive power compensation devices on the low-voltage side are converted into PV nodes by node type switching.And the quantified reactive power non-convergence index is acquired.Then,the action space and reward value of deep reinforcement learning are reasonably designed and the adjustment strategy is obtained by taking the reactive power non-convergence index as the algorithm state space.Finally,the effectiveness of the power flow convergence adjustment algorithm is verified by an actual power grid system in a province.

Transcriptional regulatory circuits controlling muscle fiber type switching

Skeletal muscle fitness plays vital roles in human health and disease and is determined by developmental as well as physiological inputs. These inputs control and coordinate muscle fiber programs, including capacity for fuel burning, mitochondrial ATP production, and contraction. Recent studies have demonstrated crucial roles for nuclear receptors and their co-activators, and micro RNAs(mi RNAs) in the regulation of skeletal muscle energy metabolism and fiber type determination. In this review, we present recent progress in the study of nuclear receptor signaling and mi RNA networks in muscle fiber type switching. We also discuss the therapeutic potential of nuclear receptors and mi RNAs in disease states that are associated with loss of muscle fitness.

Electrical switching in cadmium ferrite with different rare-earth ions(Sm^(3+), Y^(3+), and La^(3+))

The cadmium ferrite and 5 % rare-earth ions（Sm^3＋, Y^3＋, and La^3＋） added Cd ferrites were synthesized by oxalate co-precipitation method and characterized by X-ray diffraction（XRD）, Fourier transform infrared spectroscopy（FT-IR）, and scanning electron microscope（SEM） techniques. All ferrite samples under investigation exhibit current-controlled negative resistance type I–E characteristics at room temperature. The required electrical-switching field in cadmium ferrite is higher than that for 5 % Sm^3＋, Y^3＋, and La^3＋added cadmium ferrites. The5% addition of Sm^3＋, Y^3＋, and La^3＋ in cadmium ferrite is found to decrease the grain size in this ferrite. This decrement in the grain size makes the required switching field to decrease in cadmium ferrite. No aging effect for electrical switching is observed in these ferrites.