The problem of distributing gas through a network of pipelines is formulated as a cost minimization subject to nonlinear flow-pressure relations, material balances, and pressure bounds. The solution method is based on piecewise linear approximations of the nonlinear flow-pressure relations. The approximated problem is solved by an extension of the Simplex method. The solution method is tested on real-world data and compared with alternative solution methods.

The gas transmission problem solved by an extension of the simplex algorithm

Vulnerability analysis of power systems is a key issue in modern society and many efforts have contributed to the analysis. Complex network metrics for the assessment of the vulnerability of networked systems have been recently applied to power systems. Complex network theory may come in handy for vulnerability analysis of power systems due to a close link between the topological structure and physical properties of power systems. However, a pure topological approach fails to capture the engineering features of power systems. So an extended topological method has been proposed by incorporating several of specific features of power systems such as electrical distance, power transfer distribution factors and line flow limits. This paper defines, starting from the extended metric for efficiency named as net-ability, an extended betweenness and proposes a joint method of extended betweenness and net-ability to rank the most critical lines and buses in an electrical power grid. The method is illustrated in the IEEE-118-bus, IEEE-300-bus test systems as well as the Italian power grid.

Structural vulnerability of power systems: a topological approach

Controlled islanding is an active and effective way of avoiding catastrophic wide area blackouts. It is usually considered as a constrained combinatorial optimization problem. However, the combinatorial explosion of the solution space that occurs for large power systems increases the complexity of solving it. This paper proposes a two-step controlled islanding algorithm that uses spectral clustering to find a suitable islanding solution for preventing the initiation of wide area blackouts by un-damped electromechanical oscillations. The objective function used in this controlled islanding algorithm is the minimal power-flow disruption. The sole constraint applied to this solution is related to generator coherency. In the first step of the algorithm, the generator nodes are grouped using normalized spectral clustering, based on their dynamic models, to produce groups of coherent generators. In the second step of the algorithm, the islanding solution that provides the minimum power-flow disruption whilst satisfying the constraint of coherent generator groups is determined by grouping all nodes using constrained spectral clustering. Simulation results, obtained using the IEEE 9, 39 and 118-bus test systems, show that the proposed algorithm is computationally efficient when solving the controlled islanding problem, particularly in the case of a large power system.

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Two-step spectral clustering controlled islanding algorithm

An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines

Due to the widely deployed sensors in the pipeline network, the data-driven detection method is a natural choice with multiple sensor measurements. However, the incomplete data problem caused by device failure or network interruption seriously hinders the implementation of pipeline status monitoring. Aiming at this difficulty, this article proposes a generative adversarial network based on trinetworks form (tnGAN) to handle leak detection problems with incomplete sensor data. First, the generative model is proposed to recover incomplete data through fully exploiting the same-level nature similarity of data features. Therein, the same type of sensor data, obtained from the pipeline network, is used as the input. Next, to further boost the temporal evolvement characteristics and the spatial similarity, a multiview awareness strategy is incorporated in the established model to facilitate the integration of inherent information. Then, a dual-discriminative network architecture is proposed to detect pipeline status through computing the similarity of the latent features of samples. With the abovementioned structure, the proposed method can achieve different incomplete data recovery situations, such as individual lost and random missing. In addition, it can also aggregate the output and features of the discriminative networks to obtain the pipeline leak detection result. Finally, the experiment results on a pipeline network demonstrate the capability and effectiveness of the proposed method in both data recovery and leak detection.

A tnGAN-Based Leak Detection Method for Pipeline Network Considering Incomplete Sensor Data

This paper investigates the situation awareness issue of power system with massive measured data. To address this issue, first, a graph-theory-based network partitioning algorithm is proposed to realize decentralized detection in a faster response speed, while using power flow characteristics highlights the independency of different groups. Further, a hierarchical event detection method is proposed to judge voltage change and locate event position according to spectral distribution change of established multidimensional matrix. With the proposed method, the system situation can be assessed and the knowledge of the system model is not required. In addition, the accurate result of weak event happened in system could also be obtained. The simulation results are presented to illustrate the effectiveness of the proposed detection method.

A Hierarchical Event Detection Method Based on Spectral Theory of Multidimensional Matrix for Power System

In the real-time status monitoring of pipeline network, incomplete pressure data are unavoidable due to some device or communication errors. To solve this problem, a hierarchical data recovery method based on generative adversarial networks (GANs) is proposed in this article. First, a hierarchical data recovery framework is proposed to handle different numbers of incomplete data due to the structure of the semicentral pipeline network. Second, a joint attention module is presented to capture both interior nature and correlation relationships of multivariate pressure series and further guarantee the consistency of pressure data. Third, the macromicrodual discriminators are proposed to evaluate the recovery result through the combination of the local and global variation in temporal and spatial dependencies. Based on the novel structures, the proposed model is able to recover incomplete data with abnormal fluctuation values, unreasonable fixed values, or missing values. Finally, under a series of data recovery experiments, the efficiency of the proposed method is evaluated. Experimental results demonstrate that the proposed method is a practical way to ensure data recovery performance in the pipeline network.

Hierarchical Pressure Data Recovery for Pipeline Network via Generative Adversarial Networks

In terms of pipeline leak detection, the unavoidable fact is that existing data could not provide enough effective leak data to train a high accuracy model. To address this issue, this article proposes mixed generative adversarial networks (mixed-GANs) as a practical way to provide additional data, ensuring data reliability. First, multitype generative networks with heterogeneous parameter-updating mechanisms are designed to explore a variety of different solutions and eliminate the potential risks of instable training and scenario collapse. Then, based on expert experience, two data constraints are proposed to describe leak characteristics and further evaluate the quality of generated leak data in the training process. Through integrating the particle swarm optimization algorithm into generative model training, mixed-GAN has better generation performance than the conventional gradient descent algorithm. Based on the above-mentioned contents, the proposed model is able to provide satisfactory leak data with different scenarios, contributing to data quantity expansion, data credibility enhancement, and data variety enrichment. Finally, extensive experiments are given to illustrate the effectiveness of the proposed generative model for pipeline network leak detection.

Insufficient Data Generative Model for Pipeline Network Leak Detection Using Generative Adversarial Networks.

In this study, a fiber-optic sensor based on Fabry-Perot interferometer (FPI) and Mach-Zehnder interferometer (MZI) cascaded structure is proposed and demonstrated for seawater salinity and temperature simultaneous measurements. A section of hollow-core fiber (HCF) with a U-shaped defect is spliced between single-mode fibers to form FPI. The U-shaped defect inscribed by the femtosecond laser facilitates liquid flow-through and enables the salinity measurement. The MZI composes of laser-induced optical waveguides written in the cladding of the coating-retained single-mode fiber. There is an interaction between the evanescent field of the in-fiber waveguides and the coating due to the written waveguide is close to the cladding-coating interface. Part of the incident light is reflected by HCF to form the FPI spectrum, while the transmitted light in the core is coupled to the written waveguides and guided along the cladding region, which finally returned to the core to obtain the MZI spectrum. Theoretical analysis and experimental verification are carried out, and the results show that the proposed sensor offers a salinity sensitivity of 0.244 nm/‰ and a temperature sensitivity of -2.767 nm/℃. Such a cascaded interferometer has advantages of high sensitivity and compact size, which is expected to be widely used in seawater parameter measurements due to the flexible structure and precise control.

Xuguang Hu

Papers

A tnGAN-Based Leak Detection Method for Pipeline Network Considering Incomplete Sensor Data

A Hierarchical Event Detection Method Based on Spectral Theory of Multidimensional Matrix for Power System

Hierarchical Pressure Data Recovery for Pipeline Network via Generative Adversarial Networks

Insufficient Data Generative Model for Pipeline Network Leak Detection Using Generative Adversarial Networks.

Femtosecond Laser-inscribed Fiber-optic Sensor for Seawater Salinity and Temperature Measurements