As the penetration rate of distributed generators (DG) in active distribution networks (ADNs) gradually increases, it is necessary to accurately estimate the operating state of the ADNs to ensure their safe and stable operation. However, the high randomness and volatility of distributed generator output and active loads have increased the difficulty of state estimation. To solve this problem, a method is proposed for forecasting-aided state estimation (FASE) in ADNs, which integrates the optimal extreme random forest based on the maximum average energy concentration (MAEC) and variable mode decomposition (VMD) of states. Firstly, a parameter optimization model based on MAEC is constructed to decompose the state variables of the ADNs into a set of intrinsic mode components using VMD. Then, strongly correlated weather and date features in ADNs state prediction are selected using the multivariate rapid maximum information coefficient (RapidMIC) based on Schmidt orthogonal decomposition. Finally, by combining the set of intrinsic mode functions of the ADNs state, calendar rules, and weather features, an ensemble FASE method based on the extreme random tree (ERT) ensemble for the ADNs based on cubature particle filtering (CPF) is developed. An optimization model based on mean absolute error and root mean square error is established to obtain the optimal integration strategy and final estimation results. Simulation verification is performed on the IEEE 118-bus standard distribution system. The results show that the proposed method achieves higher accuracy compared to other estimation methods, with root mean square errors of 1.4902 × 10⁻⁴ for voltage magnitude and 4.8915 × 10⁻³ for phase angle.

Under high distributed generation (DG) penetration, state estimation of active distribution networks (ADNs) faces three core bottlenecks: measurement sparsity, system nonlinear coupling, and non-Gaussian noise interference, severely compromising grid operational situational awareness accuracy and reliability. To address these issues, this paper proposes a forecasting-aided state estimation (FASE) framework integrating Group-Rational enhanced Kolmogorov-Arnold Network (GLKAN) and Generalized Robust PCA Crossformer (GRformer) to synergistically optimize high-fidelity pseudo-measurement generation and accurate state estimation. First, to resolve the parameter redundancy and insufficient dynamic representation of traditional multi-layer perceptrons (MLPs), the GLKAN is proposed to reconstruct the feature extraction unit. Leveraging GLKAN's function decomposition capability and group-rational activation-based adaptability, this approach enables efficient multi-dimensional nonlinear dynamic characterization of ADNs while achieving effective parameter lightweighting. Second, to fundamentally suppress non-Gaussian noise interference on spatiotemporal feature modeling, the GRformer module is built. Its end-to-end low-rank valid component-noise residual separation significantly enhances pseudo-measurement anti-interference. Finally, GLKAN-GRformer is integrated with adaptive interpolation-based strong tracking extended Kalman filter (AISTEKF), which forms a full-link state estimation scheme covering feature extraction, noise suppression and state correction. Experiments on IEEE 33/118-bus distribution systems demonstrate that the proposed method outperforms traditional methods in estimation accuracy and non-Gaussian noise robustness, proving its technical superiority and engineering value in complex ADNs.

This study investigates the effectiveness of using conditional generative adversarial networks (CGAN) to synthesize Optical Coherence Tomography (OCT) images for medical diagnosis. Specifically, the CGAN model is trained to generate images representing various eye conditions, including normal retina, vitreous warts (DRUSEN), choroidal neovascularization (CNV), and diabetic macular edema (DME), creating a dataset of 102,400 synthetic images per condition. The quality of these images is evaluated using two methods. First, 18 transfer-learning neural networks (including AlexNet, VGGNet16, GoogleNet) assess image quality through model-scoring metrics, resulting in an accuracy rate of 97.4% to 99.9% and an F1 Score of 95.3% to 100% across conditions. Second, interpretative analysis techniques (GRAD-CAM, occlusion sensitivity, LIME) compare the decision score distribution of real and synthetic images, further validating the CGAN network’s performance. The results indicate that CGAN-generated OCT images closely resemble real images and could significantly contribute to medical datasets.

This article presents a novel method for optimal phasor measurement unit placement (OPP) for full power system observability in the presence of conventional measurements. The method considers eligible PMU placements that may be omitted by existing OPP methods and hence may provide a better solution and can guarantee that the PMU placement can restore network observability. This is achieved by creating a new observability criterion and a network transformation scheme. The new observability criterion uses injection and zero injection measurements to improve the solution and the network transformation scheme reduces the size of the OPP problem and is a prerequisite for applying the proposed observability criterion. The OPP problem is solved using binary integer linear programming (BILP) or binary integer linear programming (BILP). Therefore, the proposed OPP method can be easily combined with other OPP methods, considering other constraints/scenarios using BILP, such as OPP considering PMU current channel limits. The new method is tested using simulations of the IEEE 14-, 118-, and 2736-bus test systems. These results show that the proposed new method is feasible in terms of execution time, is free of solutions that fail to make the system fully observable, and is capable of identifying optimal placement solutions that may be overlooked by existing OPP methods.

In transient stability assessment (TSA) of power systems, the extreme scarcity of unstable scenario samples often leads to misjudgments of fault risks by assessment models, and this issue is particularly pronounced in new-type power systems with high penetration of renewable energy sources. To address this, this paper proposes a physics-constrained diffusion-based scenario expansion method. It constructs a hierarchical conditional diffusion framework embedded with transient differential equations, combines a spatiotemporal decoupling analysis mechanism to capture grid topological and temporal features, and introduces a transient energy function as a stability boundary constraint to ensure the physical rationality of generated scenarios. Verification on the modified IEEE-39 bus system with a high proportion of new energy sources shows that the proposed method achieves an unstable scenario recognition rate of 98.77%, which is 3.92 and 2.65 percentage points higher than that of the Synthetic Minority Oversampling Technique (SMOTE, 94.85%) and Generative Adversarial Networks (GANs, 96.12%) respectively. The geometric mean achieves 99.33%, significantly enhancing the accuracy and reliability of TSA, and providing sufficient technical support for identifying the dynamic security boundaries of power systems.