This study proposes a deep transfer learning method using a deep convolutional neural network pre-trained with ImageNet for transient stability assessment. The procedure of deep transfer learning, incorporating the role and considerations of the transient stability assessment system, is suggested. The transient assessment system learns the relationship between the severity of disturbances and transient stability in the power system through the proposed training method. The severity of a disturbance based on the physical causal relationship of the angle stability is proposed to train and implement the transient stability assessment model. The power system state variables obtained from the phasor measurement unit are converted into the feature map described by the severity of a disturbance, enabling the training of transient stability characteristics of the power system to the deep convolutional neural network. The training dataset is constructed using the time-domain simulation on IEEE 39 and IEEE 118-bus benchmark power system models configured in MATLAB/Simulink. As a result of deep transfer learning, which involves training with freezing some of the convolutional layers of the pre-trained deep network, the most suitable model for transient stability assessment is selected among the pre-trained deep networks. The effectiveness of the proposed method is compared with other approaches using the confusion matrix, and the robustness against noise interference is also investigated.

This paper proposes a novel structure of a power system stabilizer (PSS) to improve the stability of synchronous generators (SGs) in microgrids. Microgrids are relatively vulnerable in terms of stability due to their small size and low inertia. The rotational inertia and voltage support of SGs are highly suitable for getting over the vulnerabilities of microgrids, but there exist weaknesses in low-frequency oscillations (LFOs) and limitations of synchronization due to electromagnetic characteristics. Therefore, we study how to accommodate the features of microgrids in the PSS of SGs and further enhance present advantages. The PSS proposed in this paper not only damps out LFOs by conventional lead-lag compensation but also provides additional damping torque according to the magnitude of the perturbation using a synchronous impedance characteristic (SIC). The proposed Lyapunov energy-function-based control strategy can also increase the synchronizing power of the SG to improve transient stability. For performance verification, we use parameters obtained by the particle swarm optimization (PSO) algorithm to compare the existing PSS with the proposed one and analyze them. The effect of the proposed micro-power system stabilizer (μPSS) is analyzed through frequency response analysis, and finally, small-signal stability analysis and the performance of transient stability are verified by time-domain simulation (TDS) on MATLAB/Simulink.

Accurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-based variable ranking. In the proposed method, binary classification trees are constructed by applying the CART algorithm to a training dataset which is composed of normal and faulty samples for classifier learning then, to perform faulty variable isolation, variable importance values for each input variable are extracted from the constructed trees. The importance values for non-faulty variables are not influenced by faulty variables, because the values are extracted from the trees with decision boundaries only in the original input space; the proposed method does not suffer from smearing effect. Furthermore, the proposed method, based on the nonparametric CART classifier, can be applicable to nonlinear processes. To confirm the effectiveness, the proposed and comparison methods are applied to two benchmark problems and 250 MW drum-type steam boiler. Experimental results show that the proposed method isolates faulty variables more clearly without the smearing effect than the comparison methods.

This paper proposes a novel control strategy to improve the stability and performance of the linear feedback control (LC) by alleviating the nonlinearity of a synchronous generator (SG). This control strategy, Jacobian gain control (JGC), uses the Jacobian function (JF), which is the sensitivity between the power imbalance in an electrical power system (EPS) and the state variables of SGs, for control. The JFs composed of a combination of state variables of an SG were established by the Jacobian reflecting physical characteristics of SG control. An integrated controller, JGC, for the SG was constructed by applying the JFs to the models of the governor (GOV), excitation system (EX), and power system stabilizer (PSS), respectively. The effects of JGC were identified by frequency response analysis of a single machine infinite bus (SMIB) model. The comprehensive control performance and stability of JGC were verified through various case-by-case time-domain simulations on two-area and IEEE 39-bus system configured by MATLAB/Simulink.