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

Adaptive single-phase auto-reclosing method using power line carrier signals

This paper proposes a new method for adaptive single-phase auto-reclosing (ASPAR) that improves the rate of successful reclosing actions after the occurrence of a transient single-phase fault and prevents unsuccessful reclosing. After the occurrence of a fault, the only faulty phase is disconnected by the protection system (e.g., distance relays and circuit breakers). The proposed method deploys power line carrier (PLC) signals for determination of the secondary arc extinction time and releasing the reclosing signals to the circuit breakers. Despite the growing use of new communication systems (e.g., fibre-optic links), PLC systems are still widely used, and may not be considered for replacement in near future. Therefore, the proposed method can be utilized as an auxiliary application of PLC systems to enhance the resiliency of power grids. The simulation studies are carried out using EMTP-RV and MATLAB, and the advantages and disadvantages of the proposed method are discussed and compared with the existing ASPAR methods. According to the simulation results, the efficiency of the proposed ASPAR method is negligibly influenced by the following factors: fault location, faulty phase, system loading, transmission line transposition, shunt reactor, PLC carrier frequency, PLC operating mode, and the noises. © 2017 Elsevier Lt

A Recursive Method for Traveling-Wave Arrival-Time Detection in Power Systems

This paper proposes a novel recursive method for detecting the first arrival time (AT) of traveling waves (TWs) in power grids to enhance the fault-location methods relying on TWs. This method depends on the adaptive discrete Kalman filter. It estimates the parameters of a high time-resolution voltage or current measurement and generates residuals (innovation sequence). Both measurement noises and TWs can result in an abrupt change in the residuals. The proposed method pinpoints the probable abrupt change and distinguishes whether it is caused by noises or arriving waves. As the proposed method is recursive, it is proper for implementation in on-site digital fault locators for real-time applications. For evaluation of the proposed method, EMTP-RV and the real-time digital simulator are utilized to perform the transient simulations. The results are then analyzed in MATLAB. The proposed method and the state-of-the-art AT-detection methods in the prior literature are compared, and the sensitivity analysis demonstrates that the measurement noises and fault parameters have less influence on the proposed method efficiency in comparison to the existing AT-detection methods. © 1986-2012 IEEE

Decentralized Control Framework for Mitigation of the Power-Flow Fluctuations at the Integration Point of Smart Grids

In this paper, a decentralized control framework for reducing power-flow fluctuations at the integration point of DC smart microgrids (SMGs) is proposed. The output powers of non-dispatchable renewable energy resources are unpredictable and vary time to time. In this work, plug-in electric vehicles (PEVs) are employed as distributed energy storage systems (DESSs) in order to minimize the power-flow fluctuations at the integration point. In this regards, the proposed control system increases the charging rates of PEVs in excess power generation and reduces the charging rates in power shortage. The simulations are performed using Matlab/Simulink. According to the simulation results, the proposed method is able to lessen the fluctuations. It also reduces the dependency of SMGs on the main grid and improves the overall power quality in the main power systems as it minimizes the integration point power-flow fluctuations. © 2019 IEEE

Reducing Smart Microgrid Dependency on the Main Grid using Electric Vehicles and Decentralized Control Systems

International audienceThis paper proposes a new control system to reduce the power flow at the integration point of DC smart microgrids (SMGs) equipped with non-dispatchable renewable energy resources. The control system is fully decentralized, and it is based on the cooperative control, which requires the minimal communication infrastructure. In the proposed method, plug-in electric vehicles are utilized as distributed energy storage systems to mitigate the power-flow fluctuation. The PEVs start charging in excess of generation, and discharge in generation shortage. The proposed method decreases the dependency of SMGs on the main grid. It also improves the overall power quality in the bulk power systems by minimizing the integration point power-flow fluctuations. The proposed control system is evaluated using Matlab/Simulink. According to the simulation results, the performance of the proposed method is assessed, and its pros and cons are discussed

A Distributed Control System for Enhancing Smart-grid Resiliency using Electric Vehicles

This paper presents a decentralized control system based on cooperative control for managing the discharging rates of plug-in electric vehicles (PEVs) during islanding mode. Since energy storage systems (ESSs) are essential for the microgrids relying on renewable energies, it is assumed that the smart microgrid (SMG) is equipped with one main ESS. It is also assumed that several PEVs are connected to the grid via bidirectional controllable chargers, considering the proliferation of PEVs. In case of islanding, the proposed method controls the discharging rates of the PEVs to decrease the output power of the main ESS. This leads to an enhancement in the grid ride-through-ability, and consequently, its resiliency since the SMG can longer supply the loads in islanding mode. The proposed method is evaluated utilizing Matlab/Simulink. According to the simulation results, the advantages and disadvantages of the proposed control system are presented and discussed. © 2019 IEEE

Optimal time-current graded coordination of multistage inverse-time overcurrent relays in distribution networks

This paper proposes a method for optimal coordination of multistage overcurrent relays (OCRs) in radial distribution networks, including industrial, rural, and urban grids. The proposed method optimally coordinates multistage time-current curves (TCCs) using the genetic algorithm (GA). The proposed method is able to determine the proper TCC type (eg, moderately inverse, very inverse, and extremely inverse), pickup current, and time-setting multiplier (TSM) for each stage of TCCs. The proposed method utilizes the dynamic models of OCRs and fuses. This safeguards the relays against the maloperations resulting from the DC decaying part of fault currents and transient overcurrents caused by transformer energization or motor starting. The proposed method is evaluated using DIgSILENT Power Factory for power system analysis and Matlab for implementation of the proposed method. According to the simulation outcomes, the advantages and disadvantages of the proposed method are discussed and compared with the existing methods. © 2019 John Wiley & Sons, Ltd

Biomedical soft robots: current status and perspective

This paper reviews the current status of soft robots in biomedical field. Soft robots are made of materials that have comparable modulus of elasticity to that of biological systems. Several advantages of soft robots over rigid robots are safe human interaction, ease of adaptation with wearable electronics and simpler gripping. We review design factors of soft robots including modeling, controls, actuation, fabrication and application, as well as their limitations and future work. For modeling, we survey kinematic, multibody and numerical finite element methods. Finite element methods are better suited for the analysis of soft robots, since they can accurately model nonlinearities in geometry and materials. However, their real-time integration with controls is challenging. We categorize the controls of soft robots as model-based and model-free. Model-free controllers do not rely on an explicit analytical or numerical model of the soft robot to perform actuation. Actuation is the ability to exert a force using actuators such as shape memory alloys, fluid gels, elastomers and piezoelectrics. Nonlinear geometry and materials of soft robots restrict using conventional rigid body controls. The fabrication techniques used for soft robots differ significantly from that of rigid robots. We survey a wide range of techniques used for fabrication of soft robots from simple molding to more advanced additive manufacturing methods such as 3D printing. We discuss the applications and limitations of biomedical soft robots covering aspects such as functionality, ease of use and cost. The paper concludes with the future discoveries in the emerging field of soft robots. © 2020, Korean Society of Medical and Biological Engineering