An Efficient Δ-Circuit approach for Online Short-Circuit Calculation in Large Islanded AC Microgrids

In this manuscript, a novel Δ-circuit approach is proposed, which enables the fast calculation of fault currents in large islanded AC microgrids (MGs), supplied by inverter-based distributed generators (IBDGs) with virtual impedance current limiters (VICLs). The concept of virtual impedance for limiting the fault current of IBDGs has gained the interest of research community in the recent years, due to the strong advantages it offers. Moreover, Δ-circuit is an efficient approach, which has been widely applied in the past, for the calculation of short?circuit currents of transmission and distribution networks. However, the traditional Δ-circuit, in its current form, is not applicable in islanded MGs, due to the particular characteristics of such networks, e.g., the absence of a slack bus. To overcome this issue, a novel Δ-circuit approach is proposed in this paper, with the following distinct features: a) precise simulation of islanded MGs, b) fast computational performance, c) generic applicability in all types of faults e.g., single-line, 2-line or 3-line faults, d) simple extension to other DG current limiting modes, e.g., latched limit strategy etc. The proposed approach is validated through the time-domain software of Matlab Simulink, in a 9-bus and 13-bus islanded MG. The computational performance of the proposed fault analysis method is further tested in a modified islanded version of the IEEE 8500-node network.

An Efficient Δ-Circuit approach for Online Short-Circuit Calculation in Large Islanded AC Microgrids

In this manuscript, a novel Δ-circuit approach is proposed, which enables the fast calculation of fault currents in large islanded AC microgrids (MGs), supplied by inverter-based distributed generators (IBDGs) with virtual impedance current limiters (VICLs). The concept of virtual impedance for limiting the fault current of IBDGs has gained the interest of research community in the recent years, due to the strong advantages it offers. Moreover, Δ-circuit is an efficient approach, which has been widely applied in the past, for the calculation of short?circuit currents of transmission and distribution networks. However, the traditional Δ-circuit, in its current form, is not applicable in islanded MGs, due to the particular characteristics of such networks, e.g., the absence of a slack bus. To overcome this issue, a novel Δ-circuit approach is proposed in this paper, with the following distinct features: a) precise simulation of islanded MGs, b) fast computational performance, c) generic applicability in all types of faults e.g., single-line, 2-line or 3-line faults, d) simple extension to other DG current limiting modes, e.g., latched limit strategy etc. The proposed approach is validated through the time-domain software of Matlab Simulink, in a 9-bus and 13-bus islanded MG. The computational performance of the proposed fault analysis method is further tested in a modified islanded version of the IEEE 8500-node network.

Automotive Vulnerability Analysis for Deep Learning Blockchain Consensus Algorithm

In this study, future cars are attempting self-driving around the world. However, hacking, such as ECUs in automobiles, creates problems that are directly connected to human life. Therefore, this study wrote a paper that detects anomalies in such cars by field. As a related study, the study investigated the vulnerabilities of the automobile security committee and automobile security standards and investigated the detection of abnormalities in the hacking of geo-train cars using artificial intelligence’s LSTM and blockchain consensus algorithm. In addition, in automobile security, an algorithm was studied to predict normal and abnormal values using LSTM-based anomaly detection techniques on the premise that automobile communication networks are largely divided into internal and external networks. In the methodology, LSTM’s pure propagation malicious code detection technique was used, and it worked with an artificial intelligence consensus algorithm to increase security. In addition, Unity ML conducted an experiment by constructing a virtual environment using the Beta version. The LSTM blockchain consensus node network was composed of 50,000 processes to compare performance. For the first time, 100 Grouped Tx, 500 Channels were tested for performance. For the first time, the malicious code detection rate of the existing system was verified. Accelerator, Multichannel, Sharding, Raiden, Plasma, and Trubit values were verified, and values of approximately 15,000 to 50,000 were obtained. In this paper, we studied to become a paper of great significance on hacking that threatens human life with the development of self-driving cars in the future.

Multi-Scenario Physical Energy Storage Planning of Integrated Energy Systems Considering Dynamic Characteristics of Heating Network and Demand Response

The configuration of energy storage in the integrated energy system (IES) can effectively improve the consumption rate of renewable energy and the flexibility of system operation. Due to the high cost and long cycle of the physical energy storage construction, the configuration of energy storage is limited. The dynamic characteristics of the heating network and the demand-side response (DR) can realize the space-time transfer of energy. Although there is no actual energy storage equipment construction, it plays a similar role to physical energy storage and can be considered as virtual energy storage in IES planning. In this paper, a multi-scenario physical energy storage planning model of IES considering the dynamic characteristics of the heating network and DR is proposed. To make full use of the energy storage potential of the proposed model, the virtual energy storage features of the dynamic heating characteristics of the heating network and DR are analyzed at first. Next, aiming at the uncertainty of wind turbine (WT) and photovoltaic (PV) output, the scenario analysis method is used to describe the wind and photovoltaic power output with different probabilities. Finally, an electrothermal IES with an IEEE 33-node network and a 26-node heating network serves as an example to verify the effectiveness of the proposed model. The case study shows that the proposed model effectively reduces the physical energy storage configuration and achieves the economic trade-off between the investment cost and the operation cost.

A Sensitivity-Based Three-Phase Weather-Dependent Power Flow Approach for Networks with Local Controllers—PART II: Case Studies

<p><b>Power flow is an integral part of distribution system planning, monitoring, operation, and analysis. This two-part paper proposes a sensitivity-based three-phase weather-dependent power flow approach for accurately simulating distribution networks with local voltage controllers (LVC). This part II, firstly, presents simulation results of the proposed approach in an 8-Bus and 7-Bus network, which are validated using dynamic simulation. Secondly, simulation results for the IEEE 8500-node network are also presented. An extensive comparison is conducted between the proposed sensitivity-based approach and the other existing power flow approaches with respect to result accuracy and convergence speed. Moreover, the influence of weather and magnetic effects on the power flow results and the LVC states is also investigated. Simulation results confirm that the proposed sensitivity-based approach produces more accurate results than the existing approaches since it considers the actual switching sequence of LVCs as well as the weather and magnetic effects on the network. Moreover, the proposed algorithm exhibits accelerated convergence due to the usage of the sensitivity parameters, which makes it an important tool for distribution system analysis. </b></p>

A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier

Introduction Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data. Methods One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer [MACH-NC] and Radiotherapy in Carcinomas of Head and Neck [MARCH]), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference. Results In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression. Conclusion The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible.

Load Transfer Path Search and Its Evaluation between Networks in Consideration of the Mobile Energy Storage of Electric Vehicles

Load transfer is an important way of restoring a power supply after equipment failure or maintenance. However, current methods ignore cooperation between networks and users, and they also fail to take into account the vehicle-to-grid (V2G) potential of electric vehicles (EVs). In this paper, a load transfer scheme between transmission and distribution networks is proposed, considering the mobile energy storage capacities of electric vehicles. First, the mobility characteristic and the available discharge capacity of EVs are analyzed on the basis of the parking generation rate. Then, the breadth-first algorithm is used to search the load transfer paths within and between stations, and an edge matching method is proposed to realize the conversion between networks with different voltage levels. Lastly, the optimal combination weighting method was adopted to combine subjective and objective index weights and to evaluate power supply paths. The effectiveness of the proposed scheme is validated in a case composed of an IEEE 30-node network and an IEEE 57-node network, with four typical scenarios.