False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance of the proposed method is demonstrated through simulation using the six-bus power network in a wide area measurement system environment, as well as experimental data sets. Two FDIA scenarios, playback attack and time attack, are investigated. Experimental results are compared with the support vector machine (SVM) and artificial neural network (ANN). The results indicate that MSA yields better results in terms of detection accuracy than both the SVM and ANN when applied to FDIA detection.

This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

This article proposes a robust adaptive supertwisting sliding-mode controller (STSMC) for high-precision IM drive. First, the STSMC is developed for controlling and stabilizing the IM. However, the control performance may be influenced due to exterior disorders along with parameter variations since the IM model is not accurately known during operation. Therefore, it is essential to estimate the lumped disturbance to strength the robustness of the STSMC using system model-based disturbance estimator. Second, to optimize the control performance, a robust adaptive neural-network tracking controller is developed to evade the constraints on knowing the disorders and uncertainties upper bounds. The RANTC combines an STSMC and a recurrent radial basis function network (RRBFN) estimators. The RRBFNs are employed to approximate the IM dynamics along with estimating the optimum super-twisting control algorithms online. The RRBFN training is developed via adaptive particle swarm optimization (APSO) algorithm to attain enhanced performance in less learning iterations, which is the critical issue in real-time applications. Third, to imitate the STSMC algorithms for preserving the robust control characteristics without the necessity of the IM parameters, a direct adaptive intelligent controller (DAIC) based on Takagi–Sugeno–Kang-type recurrent Petri fuzzy-neural-network is developed. Furthermore, the online adaptive laws are derived according to Lyapunov theory, so that the system stability can be assured. The validity of control schemes is verified by simulation and experimentation. The simulation and experimental results confirm the superiority of the STSMC-SMDE, RANTC, and DAIC schemes compared with STSMC irrespective of exterior disorders along with indefinite model uncertainties.

This paper presents an improvement technique for the power quality of the electrical part of a wind generation system with a self-excited induction generator (SEIG) which aims to optimize the utilization of wind power injected into weak grids. To realize this goal, an uncontrolled rectifier-digitally controlled inverter system is proposed. The advantage of the proposed system is its simplicity due to fewer controlled switches which leads to less control complexity. It also provides full control of active and reactive power injected into the grid using a voltage source inverter (VSI) as a dynamic volt ampere reactive (VAR) compensator. A voltage oriented control (VOC) scheme is presented in order to control the energy to be injected into the grid. In an attempt to minimize the harmonics in the inverter current and voltage and to avoid poor power quality of the wind energy conversion system (WECS), an LC filter is inserted between VOC VSI and the grid. The proposed technique is implemented by a digital signal processor (DSP TMS320F240) to verify the validity of the proposed model and show its practical superiority in renewable energy applications.

This article presents a robust adaptive optimal tracking control (RAOTC) scheme for permanent-magnet synchronous motor (PMSM) servo drive with uncertain dynamics via adaptive dynamic programming (ADP) method. First, an adaptive identifier is developed to estimate the nonlinear dynamic functions of the PMSM using a functional-link neural-network. Then, the proposed RAOTC scheme is developed that combines an adaptive steady-state controller, an adaptive optimal tracking controller, and a robust controller. The adaptive steady-state controller is designed for attaining the targeted tracking response at the steady-state using the estimated nonlinear dynamics. The adaptive optimal tracking controller is designed for stabilizing the tracking error dynamics at the transient state in an optimal manner. Further, the robust controller is developed for compensating the approximation errors of neural-networks introduced by implementing the ADP technique. Accordingly, actor and critic neural-networks are employed for facilitating the online solution of the Hamilton-Jacobi-Bellman equation for approximating the adaptive optimal control (OC) laws via ADP method. Based on Lyapunov approach, the closed-loop stability of the PMSM servo drive system is proved to demonstrate that the proposed RAOTC scheme can ensure the system state tracking the targeted trajectory effectively. The proposed RAOTC scheme validation is performed via experimental analysis. From the experimental validation results, the PMSM servo drives dynamic behavior using the proposed RAOTC scheme can attain the robust and OC performance regardless the compounded disturbances and parameter uncertainties.

This paper presents a robust adaptive backstepping control (RABC) for high-speed permanent-magnet synchronous motor (HSPMSM) drive system. The proposed RABC achieves high performance operation by incorporating an ideal backstepping controller (IBC), a recurrent radial basis function neural network (RRBFNN) uncertainty observer, and a robust controller. The Lyapunov stability theorem is utilized to design the IBC as a position controller of the HSPMSM servo drive system. To enhance the disturbance rejection capability during parameter changes, certain information is needed within the backstepping control law so that the system performance would not sorely be affected. To mitigate the need for the lumped parameter uncertainties within the backstepping controller, an online adaptive observer based on RRBFNN is designed to estimate the nonlinear parameter uncertainties. Moreover, the robust controller is intended to retrieve the remaining of the RRBFNN approximation errors. To assure the stability of the proposed RABC, the Lyapunov stability analysis is used to derive the online adaptive control laws. The performance of the proposed RABC is verified by simulation and experimental analysis under different operating conditions and parameter uncertainties. The test results validate the effectiveness of the proposed RABC scheme to achieve preferable tracking performance regardless of external disturbances and parameter uncertainties.