This paper investigates anomaly diagnosis for grid-tied three-phase inverters in cyber–physical smart grids, with an emphasis on distinguishing physical IGBT open-circuit physical faults from anomalies induced by denial-of-service (DoS) cyber-attacks. A super-twisting-based second-order interval sliding-mode observer is developed to estimate three-phase currents with bounded errors in the presence of uncertainties and disturbances. Based on analytical residual relationships, fault localization is achieved using the residual sign pattern and magnitude ratios for single-switch and same-leg double-switch open-circuit faults. In contrast, DoS-induced anomalies primarily manifest as effective current attenuation without deterministic residual sign or ratio patterns, enabling fault-type discrimination. Simulation results demonstrate that the proposed method achieves reliable anomaly diagnosis within one fundamental cycle, without requiring additional sensors or training data.
Multi-Energy Complementary Systems (MECS) are integrated energy systems that incorporate renewable energy sources such as wind and solar power, combined with energy storage and conversion technologies. They aim to enhance energy utilization efficiency and ensure supply stability through synergistic optimization. Scientific investment decision-making is crucial for the low-carbon transition of regional energy systems. However, MECS investments face challenges such as high uncertainties and the fuzziness of expert evaluations. To address this question, this paper proposes a multi-criteria decision-making (MCDM) framework integrated with fuzzy theory. An evaluation system is constructed, which includes five dimensions: resources, economy, environment, society, and infrastructure. The Choquet integral is employed to handle resource indicators, Pythagorean fuzzy sets (PFS) are introduced to process qualitative evaluations, and a combined weighting approach integrating Fuzzy Weighting with Zero-Inconsistency (FWZIC) and Weights by Envelope and Slope (WENSLO) is utilized to determine criteria weights. Finally, prospect theory is fused with the Gained and Lost Dominance Score (GLDS) method for alternative ranking. An empirical study on MECS investment in Hebei Province, China, is conducted. The results indicate that the economic dimension exerts the most significant influence, and the Chengde Weichang project demonstrates the optimal comprehensive benefits. This research provides methodological references and a practical basis for MECS investment decision-making and regional energy optimization.
Aiming at the difficulty in balancing economic efficiency and islanding autonomy security during grid-connected operation of microgrids, as well as the limitation of fixed weights in traditional multi-objective optimization, this paper proposes a grid-connected interactive optimization strategy considering dynamic autonomy weights. A microgrid autonomy index is defined to quantify islanding preparedness, and a lightweight prediction network is designed to generate online weights for the three objectives of economy, security, and autonomy, so as to realize adaptive adjustment of the optimization focus. Furthermore, the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm is adopted to coordinate photovoltaics, energy storage, electric vehicle chargers, various loads, as well as power purchasing and selling, enabling decentralized decision-making. Results show that the proposed strategy achieves economic performance close to that of economic-only optimization (i.e., disregarding islanding preparedness) under grid-connected conditions without external faults, while shortening the interruption duration of critical loads by more than 72% during islanding transition caused by external grid faults. Meanwhile, the state of charge (SOC) remains strictly within the operational safety band of 20–90% throughout all dispatch cycles, complying with industry norms for battery cycle life preservation. The dynamic weights for economy, security, and autonomy are generated online by a lightweight neural network based solely on real-time system states rather than being fixed a priori, verifying the effectiveness of the proposed mechanism in achieving a context-aware trade-off among conflicting objectives.
