In this study, we propose a theoretical framework for evaluating the small-signal stability of power systems. Specifically, we derive a new necessary and sufficient condition for the small-signal stability of power systems in an analytically tractable form. Furthermore, this condition is invariant to the state dimension of synchronous generator models with varying levels of detail, effectively unifying the stability criteria for synchronous generators and grid-forming inverters. This proposal systematizes the analysis of mixed-source systems, thereby supporting more reliable decision-making in the planning and operation of future power systems.

There are various types of distributed energy resources (DERs), which are interconnected to power grids for a decarbonized society. The amount of generation and demand power fluctuates depending on weather conditions and users' lifestyles. Thus, flexible control strategies are required to coordinate for large number of DERs. However, due to concerns about data privacy or cyber security, conventional centralized control methods are not suitable because they require all information from all DERs. In this paper, we propose a decentralized control strategy that can achieve both supply/demand power control and voltage regulation in distribution power grids. In the proposed strategy, the operator of the distribution power grid provides a virtual price to each home. And each home determines its active and reactive powers according to the decentralized optimization. We evaluate the effectiveness of the proposed strategy through numerical experiments.

In this paper, we discuss the implementation of a sampled-data input consensus controller to achieve current sharing and average voltage regulation in DC microgrids with current sources. We show that an appropriate selection of controller gains enables the sampled-data controller to be implemented using information from each node's neighbors and neighbors-of-neighbors. Experimental results are also presented to validate the effectiveness of the sampled-data input controller under the proposed gain selection.

Managing the leaf-to-fruit ratio plays a major role in improving the efficiency of fruit cultivation. However, manually counting the number of fruits and leaves takes an enormous amount of time. Therefore, in this study, we proposed a method to automatically estimate the number of leaves on trees using 3D point cloud data obtained from backpack LiDAR. Since it was confirmed that there are differences in tree characteristics depending on the nature of the orchard area, a leaf number estimation model was calculated for each of two regions with different characteristics in order to improve the estimation accuracy. Leaf number estimation models were calculated using principal component regression analysis (PCR) and partial least squares regression (PLS), and the estimated models were evaluated using mean absolute percentage error (MAPE). The results showed that the MAPE of the estimated model using PCR and PLS was lower than that of ordinary weighted multiple regression analysis, with the smallest MAPE values of 12.0% in Kitagawa Village and 9.6% in Mihara Village.