Swift and precise fault location in distribution networks is paramount for minimising outage losses and expediting power restoration. This paper proposes a hybrid fault location technique based on data and model fusion, which formulates the fault localization task as an optimization problem. The technique tackles three key problems: fault classification, line identification and precise location in distribution networks. The technique hinges on the Grey Wolf Hunting (GWH) algorithm, which has significant advantages in exploring and exploiting capabilities, and the spatial contraction strategy (SCS), which significantly curtails computational expense. Simulation results demonstrate the proposed technique’s commendable performance in recognition accuracy and location precision, alongside robust noise immunity. Meanwhile, this hybird data and model fusion driven technique can be flexibly applied to different distribution networks.

Although integrated energy systems (IES) are currently modest in size, their scheduling faces strong challenges, stemming from both wind generation disturbances and the system’s complexity, including intrinsic heterogeneity and pronounced non-linearity. For this reason, a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration (MOGSOPE) is proposed to efficiently achieve the optimal solution under wind generation disturbances. The optimizer has an embedded trainable surrogate model, Deep Neural Networks (DNNs), to explore the common features of the multi-scenario search space in advance, guiding the population toward a more efficient search in each scenario. Furthermore, a multi-scenario Multi-Attribute Decision Making (MADM) approach is proposed to make the final decision from all alternatives in different wind scenarios. It reflects not only the decision-maker’s (DM) interests in other indicators of IES but also their risk preference for wind generation disturbances. A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization algorithms. With respect to numerical performance metrics HV, IGD, and SI, the proposed optimizer exhibits improvements of 3.1036%, 4.8740%, and 4.2443% over MOGSO, and 4.2435%, 6.2479%, and 52.9230% over NSGAII, respectively. What’s more, the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated, particularly in optimal scheduling of IES under wind generation disturbances.

Sparse measurements challenge fault location in distribution networks. This paper proposes a method for asymmetric ground fault location in distribution networks with limited measurements. A virtual injected current vector is formulated to estimate the fault line, which can be reconstructed from voltage sags measured at a few buses using compressive sensing (CS). The relationship between the virtual injected current ratio (VICR) and fault position is deduced from circuit analysis to pinpoint the fault. Furthermore, a two-stage recovery strategy is proposed for improving reconstruction accuracy of the current vector, where two different sensing matrixes are utilized to improve the incoherence. The proposed method is validated in IEEE 34 node test feeder. Simulation results show asymmetric ground fault type, resistance, fault position and access of distributed generators (DGs) do not significantly influence performance of our method. In addition, it works effectively under various scenarios of noisy measurement and line parameter error. Validations on 134 node test feeders prove the proposed method is also suitable for systems with more complex structure.

As wind farms are commonly installed in areas with abundant wind resources, spatial dependence of wind speed among nearby wind farms should be considered when modeling a power system with large-scale wind power. In this paper, a novel bivariate non-parametric copula, and a bivariate diffusive kernel (BDK) copula are proposed to formulate the dependence between random variables. BDK copula is then applied to higher dimension using the pair-copula method and is named as pair diffusive kernel (PDK) copula, offering flexibility to formulate the complicated dependent structure of multiple random variables. Also, a quasi-Monte Carlo method is elaborated in the sampling procedure based on the combination of the Sobol sequence and the Rosen-blatt transformation of the PDK copula, to generate correlated wind speed samples. The proposed method is applied to solve probabilistic optimal power flow (POPF) problems. The effectiveness of the BDK copula is validated in copula definitions. Then, three different data sets are used in various goodness-of-fit tests to verify the superior performance of the PDK copula, which facilitates in formulating the dependence structure of wind speeds at different wind farms. Furthermore, samples obtained from the PDK copula are used to solve POPF problems, which are modeled on three modified IEEE 57-bus power systems. Compared to the Gaussian, T, and parametric-pair copulas, the results obtained from the PDK copula are superior in formulating the complicated dependence, thus solving POPF problems.