The virtual synchronous generator (VSG) based on energy storage system is proposed to compensate the loss of inertia and damping of the power grid. Due to the introduction of inertia, virtual synchronous generator is more prone to power oscillation. In this paper, the nonlinear relationship between inertia and angular velocity is analyzed, and adaptive neural network control is first applied to VSG. Based on this concept, an adaptive control strategy is proposed in this paper. Firstly, Radial Basis Function (RBF) neural network which enjoys simple algorithm, strong ability of learning and fast learning rate, is used to adjust virtual inertia adaptively. This strategy not only improves response, but also reduces frequency overshoot in tracking the steady-state frequency. And then, based on the fixed damping ratio, the damping coefficient is tuned adaptively with the change of the inertia to further suppress power oscillation. The proposed strategy is supported by simulation results which show that the strategy has good performance in damping of oscillation.

To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.

The state of health (SOH) estimation is of great importance in the battery management system. However, the accurate real time state of health estimation is still a challenge because the capacity of the battery cannot be precisely monitored and measured in real-time. In response to this problem, this paper proposes an online SOH monitoring method for lithium-ion battery (LIB) based on the charge-discharge feature of battery in real time. Firstly, the relationship between the charge-discharge feature and SOH through principal component analysis (PCA) is analyzed, and the appropriate feature which can precisely describe the battery aging process is then selected. Secondly, as to decompose and denoise the collected charge-discharge feature data, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is applied. The major trend obtained is predicted by logistic regression based on the sliding time window (LR-STW), and the minor fluctuation is predicted by Kalman Filter (KF). Finally, an online SOH estimation method based on RBF neural network is proposed to establish the mapping relationship between feature and SOH. The proposed method was verified by NASA experimental data. The experimental results show that the proposed method can accurately predict the LIB SOH in real time, and the robustness of the proposed method is strengthened and promised.

The virtual synchronous generator (VSG) exhibits high-dimensional complexity, necessitating a tradeoff between accuracy and complexity when constructing small-signal models. To address this issue, this article proposes a data-driven modeling approach based on long short-term memory (LSTM) neural networks. The focus is on mapping relationships between electrical quantities, considering the influence of irrational factors on model accuracy. A detailed data-driven modeling approach for VSG is proposed and verified in this article. Due to the time-series correlation in the electrical data generated during VSG operation, the LSTM algorithm, known for its excellent time-series prediction capabilities, is chosen to construct the VSG data-driven model. Several complex VSG application scenarios are used to validate the effectiveness and accuracy of the proposed modeling approach. In conclusion, the LSTM-based VSG data-driven modeling outperforms small-signal models and recurrent neural network data-driven models in terms of accuracy and stability.

Brucellosis is a highly contagious zoonosis chronic infectious disease with a strong latent capability to endanger human health and economic development via direct or indirect ways. However, the existing methods for brucellosis diagnosis are time-consuming and expensive as they require a tedious experimental procedure and a sophisticated experimental device and performance. To overcome these defects, it is truly necessary to establish a real-time, on-site, and rapid detection method for human brucellosis. Here, a lateral flow immunoassay (LFIA) with a rapid, sensitive, and alternative diagnostic procedure for human brucellosis with a high degree of accuracy was developed based on blue silica nanoparticles (SiNPs), Staphylococcal protein A (SPA), and surface Lipopolysaccharide of Brucella spp. (LPS), which can be applied for rapid and feasible detection of human brucellosis. To our knowledge, this is the first report that uses blue SiNPs as a signal probe of LFIA for the rapid diagnosis of human brucellosis. The precursor of blue SiNPs@SPA such as colorless SiNPs and blue SiNPs was synthesized at first and then coupled with SPA onto the surface of blue SiNPs by covalent bond to prepare blue SiNPs@SPA as a capture signal to catch the antibody in the brucellosis-positive serum. When SPA was combined with the antibodies in the brucellosis-positive serum, it was captured by LPS on the test line, forming an antigen–antibody sandwich structure, resulting in the T line turning blue. Finally, the results showed that it is acceptable to use blue SiNPs as visible labels of LFIA, and standard brucellosis serum (containing Brucella spp. antibody at 1,000 IU/ml) could be detected at a dilution of 10⁻⁵ and the detection limit of this method was 0.01 IU/ml. Moreover, it also demonstrated good specificity and accuracy for the detection of real human serum samples. Above all, the blue SiNPs-based LFIA that we developed provides a rapid, highly accurate, and inexpensive on-site diagnosis of human brucellosis, and shows great promise in clinical diagnostics for other diseases.

Data-driven methods for estimating the state of health (SOH) of lithium-ion batteries (LIBs) are widely used by extracting health indicator (HI) from charge-discharge measurements. However, many existing HIs have shortcoming of heavy computing burden, which causes the difficulty on online implementation. Therefore, this paper proposes a novel HI called Equal Voltage Range Sampling Count Number (EVRSCN), which is used to estimate SOH of LIBs. The proposed HI is extracted from the charging process. The EVRSCN HI can be extracted online with reduced calculation burden of BMS. Gaussian Process Regression (GPR) is used to quickly achieve accurate SOH estimation based on EVRSCN. The SOH estimation which applies three typical and widely-used datasets of Oxford, SNL and CALCE shows that the proposed method can achieve a promised accuracy, and the root-mean-square error (RMSE) could lower than 0.5% in some typical cases. In addition, the noise immunity of EVRSCN has been evaluated and compared with existing HIs. The results show that the proposed EVRSCN has stable and promised SOH estimation accuracy, as well as good noise immunity.