The wide-area measurement system (WAMS) provides the synchronized dynamic measurement for power systems; however, the effective monitoring of subsynchronous oscillations (SSO) with WAMS has not been achieved. To achieve SSO monitoring based on the synchrophasors provided by WAMS, a synchrophasors-based SSO identification technique was proposed in this paper. On the basis of existed synchrophasors, this technique can obtain the frequencies and amplitudes of both the fundamental and subsynchronous components of currents and voltages in SSOs effectively. In this technique, the impacts of both the fast Fourier transform (FFT) synchrophasor algorithm and the sampling rate of synchrophasors on the spectrum of the subsynchronous component were taken into account. On one hand, the SSO frequency was obtained according to the spectrum of magnitudes of synchrophasors. On the other hand, a correction method for the spectrum of magnitudes of synchrophasors, in contrast to a constructed standard waveform that proportionally matches with the measured SSO, was applied to obtain the actual amplitudes of both the fundamental and subsynchronous components. Both the numerical simulation and the illustrations of two different actual SSO incidents were presented with comparisons to verify the correctness and feasibility of the proposed technique. This technique is of important practical value for mitigations of SSO in actual power systems.

Co‐free high‐Ni layered cathode materials LiNi_xMe_yO₂ (Me = Mn, Mg, Al, etc.) are a key part of the next‐generation high‐energy lithium‐ion batteries (LIBs) due to their high specific capacity and low cost. However, the hindered Li⁺ kinetics and the high reactivity of Ni⁴⁺ result in poor rate performance and unsatisfied cycling stability. This work designs a promising strategy for designing a high‐performance high‐entropy doping Co‐free high‐Ni layered cathode LiNi_0.9Mn_0.03Mg_0.02Ta_0.02Mo_0.02Na_0.01O₂ (HE‐Ni90‐1.557) by elemental screening and compositional fine‐tuning. Compositional fine‐tuning optimizes the synergistic relationship between the high‐entropy dopant elements, thereby significantly suppresses the kinetic hysteresis induced by Li⁺/Ni²⁺ mixing. The pillar effect significantly enhances the diffusion kinetics of Li⁺ at the high state of charge (SOC). Meanwhile, the high‐entropy fine‐tuning significantly postpones the H2‐H3 phase transition and reduces the dissolution of transition metals and the loss of lattice oxygen in the cathodes. Consequently, the diffusion kinetics of Li⁺ at the atomic and electrode particle scales are significantly enhanced. The HE‐Ni90‐1.557 cathode exhibits an initial capacity of 225.1 mAh g⁻¹ at 0.2 C and a full cell with a high capacity retention of 83.1% after 1500 cycles at 3C. This work provides a promising avenue for commercializing Co‐free high‐Ni cathodes for next‐generation LIBs.

The sub-synchronous resonance oscillations (SSO) caused by considerable renewable energy generations and power electronic devices seriously affect power system stability. Synchrophasor-based SSO parameter identification can effectively monitor SSOs. Aiming at the fundamental and frequency-coupled sub-/super-synchronous components that appear simultaneously in SSOs, an improved parameter identification algorithm is proposed in this paper based on the original interpolation Discrete Fourier Transform (DFT) and Hann-window. As the complement to the original algorithm, the improvements proposed in this paper additionally focus on the influence between the positive-/negative-frequency parts of the sub-/super-synchronous and fundamental components. Thus, the frequency, amplitude, and phase of the fundamental, sub-synchronous, and super-synchronous components can be accurately obtained by the proposed algorithm. This algorithm is compared to the original interpolated DFT and Prony analysis with both the simulated Phasor Measurement Unit (PMU) data and PMU data recorded during an actual SSO incident to verify its correctness and effectiveness. In the improved algorithm, the additional consideration for the super-synchronous component and the additional identified parameters significantly enhance the practical value of the interpolated DFT algorithm.

High impedance faults (HIFs) in distribution networks are hard to describe and be detected precisely because of the complexity and randomness of their features. Therefore, traditional feature analysis methods may lack sufficient reliability and generalization, which makes data-based methods a more appropriate option. However, according to previous statistical analyses, in practical scenarios, only a small quantity of historical HIF data (less than 20%) can be recorded and utilized. In this paper, a transfer learning-based HIF detection method is proposed under a cloud-edge collaboration framework of the Internet of Things, which can solve the problem of insufficient data by integrating historical data from multiple distribution networks. Through the cloud-edge collaboration framework, all features from different distribution networks are first integrated to form a basic cloud convolutional neural network model for HIF detection. The features are extracted and updated by edge computers based on the accurate synchronous measurements provided by distribution-level phasor measurement units. To uniform the data scales of the different distribution networks, principal component analysis is adopted during feature extraction. Specific to each distribution network, the target HIF detection model is transferred from the basic cloud model by fine-tuning. Furthermore, a data augmentation method based on locality sensitive hashing is proposed to improve the performance of the transferred model. The proposed HIF detection method can be operated in both online and offline modes. The performance was verified by seven different distribution networks in numerical simulations and one practical experimental distribution network.

The closed-loop delay is one of the most important factors affecting the control performance of a wide-area control system (WACS), due to the network transmission of feedback and control signals. A prediction based hierarchical delay compensation (PHDC) method is proposed in this letter at the data processing level to deal with delays. For the control strategy on the top-level, all feedback and control information are real-time and transparent without consideration of delay. The PHDC method compensates the random and distributed delay effectively and reduces the impact of delays on the control performance.