Due to the aging of electric infrastructures, conventional power grid is being modernized towards smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks. However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies. Therefore, in this paper, scenario based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. Then, in order to effectively detect the established cyber-attacks, an interval state estimation (ISE) based defense mechanism is developed innovatively. In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable. At last, a typical deep learning, i.e., stacked auto-encoder (SAE), is designed to properly extract the nonlinear and non-stationary features in electric load data. These features are then applied to improve the accuracy for electric load fore-casting, resulting in a more narrow width of state variables. The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution. The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks.

Mutual inductance and load condition of a wireless power transfer system are essential parameters to regulate the output voltage and power or track maximum efficiency. This paper proposes a parameter identification approach for the DC-DC WPT resonant converter only requiring the primary-side measurement to identify the mutual inductance, output voltage, output power, and efficiency under unknown varying misalignment and load conditions. The approach is widely applicable to series-series, series-parallel, parallel-series, parallel-parallel, and LCC-C topologies and can estimate the value variation of the resonant capacitors, which are the unique advantages as compared to other existing methods. Moreover, there is no restriction on the operation frequency selection. Direct-quadrature transformation technique is applied to acquire the fundamental and third-order harmonics information of primary-side voltage and current accurately. Detailed analysis, hardware design, and laboratory prototype are presented with experimental results to validate the correctness and accuracy of the proposed approach.

Successful detection of false data injection attacks (FDIAs) and removal of state bias due to FDIAs are essential for ensuring secure power grids operation and control. This paper first extends the approximate dc model of FDIA to a more general ac model that can handle both traditional and synchronized measurements. To automatically filter out the established FDIAs, we propose a state reconstruction scheme consisting of a contaminated state separation method, an enhanced bad data identification approach and a state recovery algorithm. In this scheme, a classifier is developed by aggregating a series of extreme learning machines (ELMs) to detect anomaly states caused by FDIAs. Gaussian random distribution and Latin hypercube sampling are adopted to initialize the input weights of base ELMs, which can provide more diversities to enhance the ensemble performance. Then, to identify the exact locations of the compromised measurements, a state forecasting-based bad data identification approach is proposed by exploiting the consistency between the forecasted and the received measurements. Finally, an effective state recovery algorithm applies quasi-Newton method and Armijo line search to address the possible system unobservable problem due to the removal of attacked measurements. Numerical tests on serval IEEE standard test systems verify the efficiency of the proposed FDIA model and state reconstruction scheme.

The growing diversity of consumer loads emphasizes the need for efficient operation of photovoltaic generation systems (PVGSs). This paper presents a novel control framework for maximum power point tracking (MPPT) in PVGSs, designed to enhance efficiency, robustness, and adaptability under dynamic environmental conditions. The proposed methodology integrates optimized Artificial Neural Network (ANN) with nonlinear backstepping controller. Hybrid particle swarm optimization (PSO) and genetic algorithm (GA) approach is used to optimize ANN, which leverages the global search capabilities of PSO and the local refinement strengths of GA to optimize the ANN’s weights and biases, ensuring accurate prediction of the PV reference voltage that is critical for MPPT. Additionally, the nonlinear backstepping controller ensures system stability and precise tracking of the predicted reference voltage by recursively constructing Lyapunov functions to guarantee asymptotic stability. The effectiveness of this control framework is evaluated through MATLAB/Simulink and validated with hardware testing. It is observed from simulation and hardware testing results, compared to existing ANN and conventional techniques, the proposed approach yielded significant improvements by achieving root mean square error of 0.103%, tracking accuracy of 99. 8%, and reduction of power loss to 0. 2%. The proposed control framework significantly enhances MPPT robustness and efficiency in dynamic environments and enables higher integration of renewable energy. This advancement supports scalable, resilient PV systems critical for sustainable energy transitions.

A magnetic integration method of single- and multi-stage electromagnetic interference (EMI) filters is presented. Common-mode (CM) and differential-mode (DM) inductors are integrated into a single magnetic core unit to significantly reduce the filter volume. A 500 W Silicon Carbon (SiC) single-phase inverter is used as an experimental platform. The magnetic integration model is designed, and the magnetic circuit analysis is conducted in a simulation to ensure that the core will not reach magnetic saturation under full load conditions. The feasibility of the proposed method is verified by an EMI test in a simulation and an experiment. Compared with a traditional discrete passive EMI filter (DPEF), the proposed integrated EMI filters can reduce 40% of the total volume and 69% of the inductor volume in integrated single-stage EMI filter (ISSEF) with satisfactory EMI suppression ability. Although the weight of integrated inductor of integrated multi-stage EMI filter (IMSEF) has 23.8% increment, the IMSEF shows a better EMI suppression ability and 4.6% inductor volume reduction.

In this paper, an energy management system (EMS) using intelligent Lyapunov based adaptive fuzzy controller is designed for standalone microgrid having photovoltaic and wind turbines as primary sources, while battery storage system and diesel generator are available as secondary sources. Moreover, any remnant power available in the system is guided by the controller towards the dump load. The centralized controller based on intelligent adaptive fuzzy system helps to optimize the power flow in the system during high and low penetration of renewable energy resources under variable load conditions. Additionally, voltage regulation is carried out using nonlinear Lyapunov controller which kept the output voltages in desired range. The proposed control system helps in increasing the efficiency and reliability of standalone microgrid. Furthermore, the proposed system is validated by comparing the simulation and hardware results with the PID and sliding mode controller, which are consider as benchmark controller techniques for standalone microgrid. The outcome of the system validates the superiority of proposed techniques and achieved global asymptotic stability under variable load conditions.