In this article, a new event-triggered $H_{\infty }$ load frequency control (LFC) approach with dynamic triggered algorithm (DTA) for multi-area nonlinear power systems (NPSs) based on non-fragile proportional integral control (NPI-control) strategy is addressed. Firstly, different from the existing linear single-area LFC model for power systems, an improved nonlinear multi-area model with the performance of large-scale adjustment frequency fluctuation is constructed by considering the phenomenon of overshoots and long-term oscillations. Due to the existence of control uncertainty, it is the first time that the NPI-control scheme is applied to LFC approach for NPSs. Then, the DTA is proposed to adjust the dynamic event-triggered parameters, which reduces the occupation of communication bandwidth and the data computation of NPSs. Furthermore, a modified quadratic form with time-varying matrix and two-side closed functional method are adopted to construct the relaxed Lyapunov-Krasovskii functional, where some slack matrices are unnecessarily positive definite. Based on Lyapunov method, some less-conservatism stability criteria are derived. Utilizing the linear matrix inequality toolbox, the allowable upper bound of time-varying delays and the NPI-controller are obtained. Finally, a numerical example is presented to demonstrate the availability of the approach developed in this work.

The precise temperature distribution measurement is crucial in many industrial fields, where ultrasonic tomography (UT) has broad application prospects and significance. In order to improve the resolution of reconstructed temperature distribution images and maintain high accuracy, a novel two-step reconstruction method is proposed in this article. First, the problem of solving the temperature distribution is converted to an optimization problem and then solved by an improved version of the equilibrium optimizer (IEO), in which a new nonlinear time strategy and novel population update rules are deployed. Then, based on the low-resolution and high-precision images reconstructed by IEO, Gaussian process regression (GPR) is adopted to enhance image resolution and keep the reconstruction errors low. After that, the number of divided grids and the parameters of IEO are also further studied to improve the reconstruction quality. The results of numerical simulations and experiments indicate that high-resolution images with low reconstruction errors can be reconstructed effectively by the proposed IEO-GPR method, and it also shows excellent robust performance. For a complex three-peak temperature distribution, a competitive accuracy with 3.10% and 2.37% error at root-mean-square error and average relative error is achieved, respectively. In practical experiment, the root-mean-square error of IEO-GPR is 0.72%, which is at least 0.89% lower than that of conventional algorithms.

State of health estimation is critical for battery management system.With autonomous ability and superior nonlinear mapping capability, machine learning is now a hot topic in this field. The training sample set of current machine learning methods based on single learner is often the regional data, which leads to a small range of data acquisition and affects the generalization ability of the model. Regard this issue, the idea of ensemble learning is considered, by generating differential data samples and synthesizing the output of a series of base learners, a good learning performance can be achieved. Furthermore, grey relational analysis is used for feature correlation analysis. In this work, NASA battery data sets are used to verify and validate the performance of the proposed method, which indicated an enhanced accuracy of the results based on the ensemble learning method. The proposed battery healthy assessment model based on ensemble learning can be concluded to providing of highly accurate and stable SOH predictions.

In this work, a novel dynamic-memory event-triggered $H_{\infty }$ load frequency control (LFC) approach for the power system is proposed considering the existence of hybrid attacks. A dynamic-memory event-triggered mechanism (DMETM) is first presented under denial-of-service (DoS) attacks to reduce the occupation of network communication bandwidth. Different from the existing event-triggered mechanisms (ETMs), the superiority of DMETM is that not only the past transmitted packets can be utilized but also the amount of utilized packets can be adjusted according to the state error of the power system. Then, the general LFC model of the power system is reconstructed as a switched system on account of the existence of DoS attacks and deception attacks. Based on the reconstructed switched model, an exponentially mean-square stability criterion with an $H_{\infty }$ performance index is derived by constructing appropriate Lyapunov–Krasovskii functionals (LKFs). Furthermore, the DMETM controllers and event-triggered weighting matrices can be obtained by solving the relevant linear matrix inequalities (LMIs). Finally, some illustrated examples are presented to demonstrate the feasibility and effectiveness of the approach proposed.

In this article, a novel co- design approach of observed-based fault detection filter (FDF) and dynamic event-triggered controller is proposed for multi-area wind power system under dual alterable aperiodic (DAA) denial-of-service (DoS) attacks. It is the first attempt to design an observed-based FDF for wind power system considering actuator fault signal. And a tolerable actuator fault threshold function is constructed to warn the occurrence of fault signal. Then, considering the bandwidth limitation of network communication, a novel dynamic event-triggered scheme is proposed to decrease the occupation of communication channel. Furthermore, the DAA DoS attacks presented in this work have different blocking effects on data transmission in different network channel. On account of the existence of aperiodic DoS attacks, the general load frequency control model for wind power system is reconstructed as a new switched system. Based on the reconstructed model, the stability with an $H_{\infty }$ performance index is demonstrated by constructing appropriate polynomial Lyapunov-Krasovskii functionals. Besides, the FDF and dynamic event-triggered controller can be achieved by solving linear matrix inequalities. Finally, some contradistinctive case studies are given to illustrate the feasibility and effectiveness of the approaches proposed in this article.