primary frequency
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The high proportion of renewable energy sources (RESs) in the system reduces the frequency support capacity and aggravates the generation of unbalanced power, while the dynamic frequency dispersion makes it difficult for a centralized energy storage system (ESS) to take into account the frequency requirements of different regions. In this context, the research takes the region with high penetration of RESs and frequent power fluctuations as the grid node of the ESS. By configuring the parameters of the ESS under the control strategy of virtual synchronous generators, the inertia and the primary frequency reserve of the system are supplemented, and the regulation characteristics of the ESS are depicted. Taking the steady-state recovery time and the amplitude coefficient as the evaluation indexes, the effects of the virtual inertia constant, the virtual damping coefficient, and the virtual frequency regulation coefficient on the behavior of the ESS are deeply analyzed. Finally, the quantitative configuration of the ESS is realized by considering the frequency response and the dynamic frequency dispersion.

For an electric power grid that has large penetration levels of variable renewable energy including wind generation and photovoltaics, the system frequency stability is jeopardized, which is manifest in lowering frequency nadir and settling frequency. This paper suggests an enhanced primary frequency response strategy of a doubly-fed induction generator (DFIG) in association with pitch angle control. The DFIG works in de-loaded operation with a certain reserve power via pitch angle control prior to disturbances for frequency regulation. To address this, a function of the pitch angle is employed that decreases the pitch angle with time to slowly feed the active power to the power gird. The simulation results demonstrate the effectiveness and feasibility of the proposed primary frequency response strategy including the settling frequency and frequency nadir.

Abstract Neural oscillations are the rich source to understand cognition, perception, and emotions. Decades of research on brain oscillations have primarily discussed neural signatures for the western classification of emotions. Despite this, the Indian ancient treatise on emotions popularly known as Rasas has remained unexplored. In this study, we collected Electroencephalography (EEG) encodings while participants watched nine emotional movie clips corresponding to nine Rasas. The key objective of this study is to identify the brain waves that could distinguish between Rasas. Therefore, we decompose the EEG signals into five primary frequency bands comprising delta (1-4 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz). We construct the functional networks from EEG time-series data and subsequently utilize the fourteen graph-theoretical measures to compute the features. Random Forest models are trained on the extracted features, and we present our findings based on classifier predictions. We observe slow (delta) and fast brain waves (beta and gamma) exhibited the maximum discriminating features between Rasas, whereas alpha and theta bands showed fewer distinguishable pairs. Out of nine Rasas, Sringaram, Bibhatsam, and Bhayanakam displayed the most distinguishing characteristics from other Rasas. Interestingly, our results are consistent with the previous studies, which highlight the significant role of higher frequency oscillations for the classification of emotions. Our finding on the alpha band is consistent with the previous study, which reports the maximum similarity in brain networks across emotions in the alpha band. This research contributes to the pioneering work on Indian Rasas utilizing brain responses.

Owing to rapid increase in PV penetration without inherent inertia, there has been an unremitting deterioration of the effective inertia of the existing power systems. This may pose a serious threat to the stability of power systems during disturbances if not taken care of. Hence, the problem of how to emulate Synthetic Inertia (SI) in PV Systems (PVS) to retain their frequency stability demands attention. Super Capacitor (SC)-based storage become an attractive option over the other energy storage types because of its high-power density, burst power handling capability, faster response and longer life cycle. Considering this, the authors here propose a novel PV-SC Cascaded Topology (PSCT) as a cost-effective approach to emulate SI by integrating a low voltage SC to a high voltage grid-connected PVS. The proposed PSCT helps in operating the SC as a voltage source rather than a current source. Thus, it eliminates the high gain requirements of the SC interfacing converters. The aim is to target two main frequency response services, i.e., Primary Frequency Response (PFR) and Synthetic Inertial Response (SIR), using a novel common control scheme, but without affecting any other energy intensive services. The authors introduced a Droop-Inspired (DI) method with an adjustable inertia constant to emulate dynamic inertia so that a wider range of Rate of Change of Frequency (RoCoF) values can be serviced with a limited storage. A very streamlined analysis was also carried out for sizing of the SC stage based on a simple Three-Point Linearization (TPL) technique and DI technique with a limited knowledge of the disturbance parameters. The whole system was initially validated in a MATLAB Simulink environment and later confirmed with the OPAL-RT Real-Time Simulator. The investigated response was subject to variation in terms of control parameters, changes in solar irradiance, grid frequency variation, etc.