Establishing cleaner energy generation therefore improving the sustainability of the power system is a crucial task in this century, and one of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies such as wind, solar, and nuclear. However, with the increasing number of heterogeneous components included, the complexity of the hybrid energy system becomes more significant. And the complex system imposes a more stringent requirement of the contingency plan to enhance the overall system resilience. Among different strategies to ensure a reliable system, intentional islanding is commonly applied in practical applications for power systems and attracts abundant interest in the literature. In this study, we propose a hierarchical spectral clustering-based intentional islanding strategy at the transmission level with renewable generations. To incorporate the renewable generation that relies on the inverter technology, the frequency measurements are considered to represent the transient response. And it has been further used as embedded information in the clustering algorithm along with other important electrical information from the system to enrich the modeling capability of the proposed framework. To demonstrate the effectiveness of the islanding strategy, the modified IEEE-9 bus and IEEE-118 bus systems coupled with wind farms are considered as the test cases.

This paper proposes a resilient and secure configuration for coastal distribution grids by integrating the security constraint unit commitment (SCUC) and mobile marine microgrids (MMMGs). In the proposed configuration, MMMGs can be connected to the coastal distribution grids in both normal and post-disaster operations. It is assumed that both MMMGs and SCUC networks include both dispatchable (e.g., gas turbines and diesel generators) and nondispatchable generators (e.g., photovoltaics and wind turbines). The proposed problem consists of realistic formulations that seek to minimize the total MMMGs and SCUC operation costs, while maximizing distribution grid resiliency. A heuristic technique, known as the collective decision optimization algorithm, is employed to address the complexity and nonlinearity of the formulated problem. Moreover, the unscented transform technique is adopted to model the uncertainties associated with renewable energy sources output and load demand. To show the effectiveness and merits of the proposed configuration, the IEEE 69-bus distribution network is selected and tested for both normal and post-disaster operations.

Accurate and robust defect diagnosis methods of relay protection systems could enhance power systems safety and prevent casualties. It is challenging for traditional defect diagnosis methods, such as Iterative Dichotomiser 3 (ID3) and Classifier 4.5 (C4.5), to effectively solve the problem with missing or ambiguous data that significantly affect the accuracy of diagnosis. To solve these challenges, a novel defect diagnosis method based on least square support vector machine Bayesian network decision tree (LSSVM-BNDT) is proposed in this article. LSSVM is first adopted to fill the missing data. Then, to deal with the nonexclusive ambiguous data, a multistate Bayesian network is integrated into a decision tree to make a posterior correction. The metrics of ten-fold receiver-operating characteristic cross validation and analysis of means are employed to evaluate the effectiveness of the proposed LSSVM-BNDT model. Cases studies are performed to analyze a variety of equipment, including remote terminal unit, measure and control devices, protective devices, merging units, interchangers, network analyzers, and fault recorders. Results show that the proposed LSSVM-BNDT has improved detection accuracy by up to 14.87% and 8.56% compared with the ID3 and C4.5 benchmark models, respectively.

This paper investigates the impact of uncoordinated, coordinated, and smart charging of plug-in hybrid electric vehicles (PHEVs) on the optimal operation of microgrids (MGs) incorporating the dynamic line rating (DLR) security constraint. The DLR constraint, particularly in the islanding mode, influences the ampacity of MG feeders, when distribution lines reach their maximum capacity. To overcome any line outage or contingency situation, smart PHEVs are utilized to help improve the grid security. However, using PHEVs can cause higher power losses and feeder overloading issues. To address these concerns, a reconfiguration technique is employed in this paper. A heuristic algorithm, known as the collective decision-based optimization algorithm, is utilized to overcome the non-convexity and nonlinearity of the problem. The unscented transform technique is employed to model DLR uncertainties caused by solar radiation, load demand, and weather temperature, as well as PHEVs' uncertainties caused by varying charging strategies, numbers of PHEVs being charged, charging start time, and charging duration. Moreover, a deep learning gated recurrent unit technique is designed to forecast renewable power output for mitigating the uncertainties in renewable energy components. A modified IEEE 33-bus test network is deployed to evaluate the efficiency and performance of the proposed model.

This paper aims to investigate the optimal scheduling of stochastic reconfigurable hybrid ac-dc microgrid (MG) in the presence of renewable energies and also considering dynamic line rating (DLR) constraint. DLR is a practical limitation that can potentially affect the ampacity of lines, particularly in the islanded mode when the lines reach their maximum capacity in lack of main generation source at the point of interconnection with the utility. In order to prevent overloading of the lines, the reconfiguration technique is developed to change the topology of the network by some prelocated switches. A linearization technique is adapted to address the nonlinearity of both nodal ac power flow and the DLR constraints. The unscented transform technique is utilized to model uncertainties including renewable energy generations, hourly load demands, and hourly market prices along with the DLR uncertainties such as solar radiation, wind speed, and ambient temperature. Finally, a sensitivity analysis is performed to see the effect of wind speed and solar radiation on the energy management of hybrid ac-dc MG. The performance of the proposed methodology is examined on a modified IEEE-33 bus test system, which demonstrates the high efficiency and importance of the proposed techniques in minimizing the hybrid ac-dc MG operation cost while all of the constraints of the network are satisfied.

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the important information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.