Clouds are one of the leading causes of sun shading, which reduces the direct horizontal irradiance and curtails the photovoltaic (PV) power. It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon (second/minute). To achieve the precise forecasting of cloud cover, an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue. An optimal threshold estimation method is further designed to achieve higher cloud identification precision. Considering the cloud's meteorological properties, a random hypersurface model (RHM) based on the Gaussian mixture probability hypothesis density (GM-PHD) filter is applied to track the cloud. The GM-PHD can track the rotation and diffusion of clouds, which helps to estimate sun-cloud collision. Furthermore, a hybrid autoregressive integrated moving average (ARIMA) and backpropagation (BP) neural network-based model is applied for intra-hour PV power forecasting. The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power, improving forecasting precision.

This paper explores the approximate solutions of AC power flow model with intractable nonlinearities, which significantly relaxes the strong condition of pre-estimated states for false data injection (FDI) attacks in power distribution systems. The main idea is to solve for complex-valued perturbations around a priori determined nominal voltage without neglecting quadratic terms in nonlinear power flow equations. We prove that based on power injection measurements, the approximate perturbations of nodal voltage can be more accurately obtained by a recursive approximation that considers more nonlinear components, compared with those from power-flow measurements. For FDI attacks, the strong requirement of pre-estimated states for constructing attack vector is relaxed by approximate states, while reflecting the intent of attackers and passing a bad data detection scheme in power systems. Simulation results based on a symmetric balanced 56-node testbed verify the performance of the proposed nonlinear approximation in terms of accuracy and FDI attacks effect, compared with the commonly used linear method.

For islanded microgrids (MGs), distributed control is regarded as a preferred alternative to centralized control for the frequency restoration of MGs. However, distributed control with successive communication restricts the efficiency and resilience of the control system. To address this issue, this paper proposes a distributed event-triggered control strategy for the frequency secondary control in islanded MGs. The proposed event-triggered control is Zeno behavior free and enables each DG to update and propagate its state to neighboring DGs only when a specific "event" occurs, which significantly reduces the communication burden. Compared with the existing event-triggered control, a trigger condition checking period of the proposed event-triggered control is provided to reduce the computation burden when checking the trigger condition. Furthermore, using the aperiodicity and intermittent properties of the communication, a simple detection principle is proposed to detect and isolate the compromised communication links in a timely and economic fashion, which improves the resilience of the system against FDI attacks. Finally, the control effectiveness of the proposed control scheme is validated by the simulation results of the tests on an MG with 4 DGs.

Nowadays, high penetration of wind power is integrated into power grids, and WTs usually adopt the MPPT algorithm to maximize power output, which decouples the rotor speeds of wind turbines (WTs) and system frequency. Therefore, WTs cannot provide frequency support like conventional generators. To that end, especially avoiding WTs aggravating excessive power generation during over-frequency events, optimal droop control is proposed to reduce power output by fully utilizing WTs' own potential in accelerating rotors. Due to unreliable communication in a wind farm, a game theory-based distributed rotor kinetic energy optimization model is developed to obtain the ideal WT rotor speed and power reduction. Next, the optimal droop gains for WTs are designed to be proportional to their ideal power reduction. Then, not only the frequency support capability of WTs is fully activated, but also as much wind power as possible will be stored as kinetic energy into the accelerated rotor blades. Finally, the effectiveness and rationality of the proposed control are verified in MATLAB and DIgSILENT.