Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the EMTP simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the WSCC 9-bus system.Comment: 9 pages, 7 figure

High-speed simulation of PCB emission and immunity with frequency-domain IC/LSI source models

Some recent results from research conducted in the EMC group at Okayama University are reviewed. A scheme for power-bus modeling with an analytical method is introduced. A linear macro-model for ICs/LSIs, called the LECCS model, has been developed for EMI and EMS simulation. This model has a very simple structure and is sufficiently accurate. Combining the LECCS model with analytical simulation techniques for power-bus resonance simulation provides a method for high-speed EMI simulation and decoupling evaluation related to PCB and LSI design. A useful explanation of the common-mode excitation mechanism, which utilizes the imbalance factor of a transmission line, is also presented. Some of the results were investigated by implementing prototypes of a high-speed EMI simulator, HISES. </p

A ripple reduction method for a two stages battery charger with multi-winding transformer using notch filter

This paper presents a two-stage battery charger consisting of a bridgeless Totem-pole power factor correction (TP-PFC) circuit and a full bridge converter with a multi-winding transformer. By using this transformer the cell equalizing operation can be achieved with no additional circuitry. In addition, a double-line frequency ripple reduction method is proposed to address the low frequency current ripples issues existing in both primary and secondary winding of the transformer which is caused by the voltage ripples across the intermediate DC link bus. Control and analysis of the converter at different operation modes is illustrated in detail and simulation results validate the effectiveness of the proposed converter and control algorithm

A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System