With the increasing popularization of a three-level neutral point clamped (NPC) inverter, realizing an accurate and fast fault diagnosis is also required. However, most methods are either not accurate enough or require a long period of time to collect signals as input. To compensate for these drawbacks, a novel 1-D convolution neural network (CNN) with the improved stochastic gradient optimization method is proposed in this article. First, the input data are segmented to ensure that the fault diagnosis can be completed if the current of a period is collected at any time in a period. Then, the feature of the waveform is extracted by the proposed network. In the meantime, the improved Adamod (IAdamod) method is implemented to better adaptively adjust the learning rate during the training process. Specifically, the learning rate is bigger at the beginning and smaller at the end. Finally, the classifier softmax is used for classification. The experimental results demonstrate the effectiveness of the proposed method in detecting and locating the faulty devices, which has shown that it perfectly realizes the fault diagnosis with 100% accuracy and can be diagnosed online with the time of less than 0.25 ms, far less than one cycle. The proposed method is also compared the traditional methods, and shows a better performance both in speed and accuracy, the loss can be less than 0.5 in iteration 220 and achieve zero in 1000.

The Cauer thermal network has made a realistic physical representation of thermal behaviour of a multi‐chip module (MCM) theoretically possible. However, the traditional method assumes the heat conduction is critical, with an approximate one‐dimensional heat conduction path for calculation and fixed heat spreading angle to make up for the compensation brought by the approximation. It will limit the accuracy and cause a significant error in estimating the junction temperature of MCM. In this study, the heat spreading angle in the area of two adjacent chips is studied by using a cross‐section to change the problem to a two‐dimensional heat transfer question. Based on the heat flow got from the results of calculation by using Fourier's law, an expression for heat spreading angle as a function of boundary conditions is given, which is capable of estimating the junction temperature by adjusting effective heat conduction area of an MCM whose thermal behaviour is seriously coupled among neighbour chips. The accuracy of the proposed method and expression is validated by both the finite‐element method and experimental results and they both show a good agreement with the calculation.

To mitigate the impact of photovoltaic (PV) power generation uncertainty on power systems and accurately depict the PV output range, this paper proposes a quantile regression probabilistic prediction model (TCN-QRBiLSTM) integrating a Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). First, the historical dataset is divided into three weather scenarios (sunny, cloudy, and rainy) to generate training and test samples under the same weather conditions. Second, a TCN is used to extract local temporal features, and BiLSTM captures the bidirectional temporal dependencies between power and meteorological data. To address the non-differentiable issue of traditional interval prediction quantile loss functions, the Huber norm is introduced as an approximate replacement for the original loss function by constructing a differentiable improved Quantile Regression (QR) model to generate confidence intervals. Finally, Kernel Density Estimation (KDE) is integrated to output probability density prediction results. Taking a distributed PV power station in East China as the research object, using data from July to September 2022 (15 min resolution, 4128 samples), comparative verification with TCN-QRLSTM and QRBiLSTM models shows that under a 90% confidence level, the Prediction Interval Coverage Probability (PICP) of the proposed model under sunny/cloudy/rainy weather reaches 0.9901, 0.9553, 0.9674, respectively, which is 0.56–3.85% higher than that of comparative models; the Percentage Interval Normalized Average Width (PINAW) is 0.1432, 0.1364, 0.1246, respectively, which is 1.35–6.49% lower than that of comparative models; the comprehensive interval evaluation index (I) is the smallest; and the Bayesian Information Criterion (BIC) is the lowest under all three weather conditions. The results demonstrate that the model can effectively quantify and mitigate PV power generation uncertainty, verifying its reliability and superiority in short-term PV power probabilistic prediction, and it has practical significance for ensuring the safe and economical operation of power grids with high PV penetration.

With the large-scale integration of distributed photovoltaic generation (PV) into modern distribution networks, the inherent stochasticity and volatility of renewable energy outputs have imposed non-negligible impacts on the secure and economic operation of power systems. Conventional probabilistic power flow (PPF) methods are limited in accurately modeling source–load uncertainty and, more importantly, in capturing complex nonlinear and time-varying dependence among multiple renewable energy sources. To address these issues, this paper proposes a novel PPF calculation framework based on advanced source-load modeling and time-varying D-vine Copula. Firstly, an enhanced finite mixture Beta model and a Gaussian cluster mixture model are developed to characterize the uncertainty of PV output and load demand, respectively. Secondly, a time-varying D-vine Copula model based on the generalized autoregressive score framework is constructed. And a two-stage regularized profile likelihood estimation method is proposed to estimate correlation parameters, capturing the dynamic nonlinear dependence among multiple PV generators. Finally, the de-randomized Sobol sequence-based Quasi-Monte Carlo method is adopted to perform stochastic power flow calculation. Simulation results on a real-world 129-bus distribution system in East China verify the accuracy and effectiveness of the proposed method.