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This paper proposed an ANN (Artificial Neural Network) controller to damp out inter-area oscillation of a power system using BESS (Battery Energy Storage System). The conventional lead-lag controller-based PSSs (Power System Stabilizer) have been designed using linear models usually linearized at heavy load conditions. This paper proposes a non-linear ANN based BESS controller as the ANN can emulate nonlinear dynamics. To prove the performance of this nonlinear PSS, two linear PSS are introduced at first which are linearized at the heavy load and light load conditions, respectively. It is then verified that each controller can damp out inter-area oscillations at its own condition but not satisfactorily at the other condition. Finally, an ANN controller, that learned the dynamics of these two controllers, is proposed. Case studies are performed using PSCAD/EMTDC and MATLAB. As a result, the proposed ANN PSS shows a promising robust nonlinear performance.Energies 2019, 12, 3372 2 of 13 which are linearized at the heavy load and light load condition respectively. And then it is verified that each controller can damp out inter-area oscillations at its own condition but not satisfactorily at the other condition. Finally, an ANN controller, that learned the dynamics of these two controllers is proposed. Case studies are performed using PSCAD/EMTDC and MATLAB.As a result, the proposed ANN PSS shows a promising robust nonlinear performance. Although mathematical analysis of the internal mechanism of ANN is impossible, the result implies the application of ANN will be promising in power system control problems. Lead Lag Controller for Damping Inter-Area Mode in 2 Area 4 Machine Benchmark ModelThe power system of each country is different in size and composition, and the inter-area oscillation occurs very rarely. As shown in Figure 1, the structure of the IEEE 2Area-4Machine Benchmark model has a small and simple structure but it is well suited for the study using real power system parameters [2]. Therefore, many researchers are publishing the results of research based on the IEEE benchmark model. In order to implement a case study, the test of the power system with an inter-area mode was constructed by using the PSCAD/EMTDC. And detailed parameters used in PSCAD/EMTDC are described in Appendix A.In this paper, the proposed controller is compared with the conventional controller. As shown in Figure 2, the inter-area oscillation can be confirmed from simulation results when a three-phase ground fault occurs at 1 [sec] (duration of fault is 0.

As mobile robots perform long-term operations in large-scale environments, coping with perceptual changes becomes an important issue recently. This paper introduces a stochastic variational inference and learning architecture that can extract condition-invariant features for visual place recognition in a changing environment. Under the assumption that a latent representation of the variational autoencoder can be divided into condition-invariant and condition-sensitive features, a new structure of the variation autoencoder is proposed and a variational lower bound is derived to train the model. After training the model, condition-invariant features are extracted from test images to calculate the similarity matrix, and the places can be recognized even in severe environmental changes. Experiments were conducted to verify the proposed method, and the experimental results showed that our assumption was reasonable and effective in recognizing places in changing environments.

Localization is one of the essential process in robotics, as it plays an important role in autonomous navigation, simultaneous localization, and mapping for mobile robots. As robots perform large-scale and long-term operations, identifying the same locations in a changing environment has become an important problem. In this paper, we describe a robust visual localization system under severe appearance changes. First, a robust feature extraction method based on a deep variational autoencoder is described to calculate the similarity between images. Then, a global sequence alignment is proposed to find the actual trajectory of the robot. To align sequences, local fragments are detected from the similarity matrix and connected using a rectangle chaining algorithm considering the robot’s motion constraint. Since the chained fragments provide reliable clues to find the global path, false matches on featureless structures or partial failures during the alignment could be recovered and perform accurate robot localization in changing environments. The presented experimental results demonstrated the benefits of the proposed method, which outperformed existing algorithms in long-term conditions.