As a crucial component of power systems, distribution transformers are indispensable to ensure the sustainability of power supply. In addition, unhealthy transformers can lead to wasted energy and environmental pollution. Thus, accurate assessments and predictions of their health statuses have become a top priority. Unlike assumed ideal environments, however, some complex data distributions in practical scenarios lead to more difficulties in diagnosis. One challenge here is the potential imbalanced distribution of data factors since sparsely occurring factors along with some Unusual High-Risk (UHR) components, whose appearance may also damage transformer operations, can easily be neglected. Another is that the importance weight of data components is simply calculated according to their frequency or proportion, which may not always be reasonable in real nonlinear data scenes. With such motivations, this paper proposes a novel integrated method combining the Two-fold Conditional Connection Pattern Recognition (TCCPR) and Component Significance Diagnostic (CSD) models. Initially, the likely environmental factors that could result in distribution transformer heavy overloads were incorporated into an established comprehensive evaluation database. The TCCPR model included the UHR time series and factors that are associated with heavy overload in both spatial and temporal dimensions. The CSD model was constructed to calculate the risk impact weights of each risky component straightforwardly, in line with the total risk variation levels of the whole system caused by them. Finally, the results of one empirical case study demonstrated their adaptation capability and enhanced performance when applied in complex and imbalanced multi-source data scenes.

To ensure a stable and reliable power supply, the valid and timely response of protective relays are indispensable. Through the prevention of fault expansions, potential equipment damage or system collapse can be averted, where their setting is one vital prerequisite for such effective implementations. However, the increasing complexity of distribution power systems results in more challenges for protection tuning strategies. Ergo, this paper presents an ensemble that combines the independent factor evaluation (IFE) and quantum genetic optimization (QGO) models to further optimize the performance of relays according to their distributed tuning environment. In this ensemble, both near and far-end fault characteristics can be incorporated. In the first stage, the IFE dimensional reduction model is deployed for massive heterogeneous input data, where the statistical independence of input signals is calculated, the linear transformation matrix to decouple mixed signals is found, the linear combination of such signals is formed, and the non-Gaussian property to sort them is established. This can ameliorate the following calculation efficiency under those high-dimensional data scenarios. Subsequently, the QGO model is designed to further improve relay settings, where qubit representation is built to reduce required chromosomes, the linear superposition of the optimal solution probability in different states is implemented for a better diversity and convergence performance, and a self-adaption quantum gate is established to dynamically update the qubit chromosome groups and two-state solution combinations. Lastly, an empirical case study is presented, which validates the enhanced convergence, accuracy, and rapidity of the proposed ensemble.

As one representative smart energy infrastructure in smart cities, an integrated energy system (IES) consists of several types of energy sources, thus making more complicated coupling connections between the supply and demand sides than a power grid. This will impact when allocating different energy sources to ensure the appropriate energy utilization in the IES. With this motivation, an IES energy configuration optimization strategy based on a multi-model ensemble is proposed in this paper. Firstly, one coupling model is constructed to assess the underlying collaborative relationships between two sides for a renewable-energy-connected IES. Next, the independent component analysis (ICA) method is implemented for noise reduction in massive heterogeneous input databases, which can effectively improve the computing efficiency under such high-dimensional data conditions. Also, the self-adaptive quantum genetic model (SAQGM) is built for subsequent configuration optimization. Specifically, the quantum bit representation is incorporated to reduce computation complexity in multi-states scenarios, the double-chain formation of chromosomes is deployed to diminish the uncertainty when encoding, and the dynamic adaptation quantum gate is established to successively amend parameters. Finally, an empirical case study is conducted which can demonstrate the benefits of this strategy in terms of feasibility, efficiency, and economy.

Due to the complex topology, multi-line branches, and dense spatial distribution characteristics of a distribution network, potential disturbances and failures cannot be eliminated in real scenes, which means that higher levels of both reliability and stability are required in its corresponding protection system. For this reason, the timely monitoring and pinpoint identification of an underlying abnormal operation status in those protection systems must be ensured. To this end, a data-driven-based real-time anomaly detection ensemble is proposed in this paper. First, the kernel principal components investigation (KPCI) process is deployed to compress the dimensionality of input data, which can reduce the computational complexity within such high-dimensional data environments. Next, the isolated forest (IF) model is applied to excavate potential outliers according to the numeric range of the normal operating states of different features. Thus, a better detection performance in biased or sparse distributions can be achieved by reacting swiftly to those outliers. Finally, the operation data of the power distribution network protection system in a certain area is used as a simulation case. It is evident that compared with the single model IF detection method, combining the IF with the data dimension reduction model can effectively reduce data complexity. Due to the addition of kernel functions, KPCI can adapt to high-dimensional data environments better than standard PCI, and it also has certain advantages in calculation efficiency. This validates the theory that the proposed model has a high level of anomaly detection in practical applications, can assist in the automatic identification of and response to power distribution network security risks, effectively dig out potential system operational disturbances and state abnormalities, and achieve real-time anomaly monitoring and early warning.