scholarly journals Research on Coordinated Development of a Railway Freight Collection and Distribution System Based on an “Entropy-TOPSIS Coupling Development Degree Model” Integrated with Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yun Jing ◽  
Si-Ye Guo ◽  
Xuan Wang ◽  
Fang-Qiu Chen
Keyword(s):  
Machine Learning ◽  
Distribution System ◽  
Information Acquisition ◽  
High Speed ◽  
Distribution Systems ◽  
Target System ◽  
Railway Transportation ◽  
Railway Freight ◽  
Transportation Capacity ◽  
Heavy Haul

In recent years, with the gradual networking of high-speed railways in China, the existing railway transportation capacity has been released. In order to improve transportation capacity, railway freight transportation enterprises companies have gradually shifted the transportation of goods from dedicated freight lines to passenger-cargo lines. In terms of the organization form of collection and distribution, China has a complete research system for heavy-haul railway collection and distribution, but the research on the integration of collection and distribution of the ordinary-speed railway freight has not been completed. This paper combines the theories of the integration of collection and distribution theory, coordination theory, and coupling theory and incorporates the machine learning fuzzy mathematics to construct an “Entropy-TOPSIS Coupling Development Degree Model” for dynamic intelligent quantitative analysis of the synergy of railway freight collection and distribution systems. Finally, we take the Tongchuan Depot of “China Railway Xi’an Group Co., Ltd.” as a research object to construct a target system and use the intelligent information acquisition system to collect basic data. The analysis results show that through the coordinated control of the freight collection and distribution system, the coordination between the subsystems of the integrated freight collection and distribution system is increased by 5.94%, which verifies the feasibility of the model in the quantitative improvement of the integration of collection and distribution system. It provides a new method for the research of integrated development of railway freight collection and distribution.

scholarly journals The potential for health risks from intrusion of contaminants into the distribution system from pressure transients

2003 ◽  
Vol 1 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Mark W. LeChevallier ◽  
Richard W. Gullick ◽  
Mohammad R. Karim ◽  
Melinda Friedman ◽  
James E. Funk
Keyword(s):  
Health Risks ◽  
Negative Pressure ◽  
Distribution System ◽  
High Speed ◽  
Distribution Systems ◽  
Transient Pressure ◽  
Pressure Transients ◽  
Data Loggers ◽  
Human Viruses ◽  
Treated Drinking Water

The potential for public health risks associated with intrusion of contaminants into water supply distribution systems resulting from transient low or negative pressures is assessed. It is shown that transient pressure events occur in distribution systems; that during these negative pressure events pipeline leaks provide a potential portal for entry of groundwater into treated drinking water; and that faecal indicators and culturable human viruses are present in the soil and water exterior to the distribution system. To date, all observed negative pressure events have been related to power outages or other pump shutdowns. Although there are insufficient data to indicate whether pressure transients are a substantial source of risk to water quality in the distribution system, mitigation techniques can be implemented, principally the maintenance of an effective disinfectant residual throughout the distribution system, leak control, redesign of air relief venting, and more rigorous application of existing engineering standards. Use of high-speed pressure data loggers and surge modelling may have some merit, but more research is needed.

scholarly journals An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 887 ◽  
Author(s):  
Alexandru Predescu ◽  
Ciprian-Octavian Truică ◽  
Elena-Simona Apostol ◽  
Mariana Mocanu ◽  
Ciprian Lupu
Keyword(s):  
Machine Learning ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution System ◽  
Metropolitan Areas ◽  
Operating Conditions ◽  
Multiple Model ◽  
Smart Water ◽  
Model Control

Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming.

scholarly journals Estimating the Distributed Generation Unit Sizing and Its Effects on the Distribution System by Using Machine Learning Methods

2021 ◽  
Author(s):  
Mikail Purlu ◽  
Belgin Emre Turkay
Keyword(s):  
Machine Learning ◽  
Distributed Generation ◽  
Distribution System ◽  
Distribution Systems ◽  
Load Flow ◽  
Machine Learning Algorithms ◽  
Estimation Methods ◽  
Percentage Error ◽  
Support Vector ◽  
Positive Effects

Many approaches about the planning and operation of power systems, such as network reconfiguration and distributed generation (DG), have been proposed to overcome the challenges caused by the increase in electricity consumption. Besides the positive effects on the grid, contributions on environmental pollution and other advantages, the rapid developments in renewable energy technologies have made the DG resources an important issue, however, improper DG allocation may result in network damages. A lot of studies have been practised with analytical and heuristic methods based on load flow for optimal DG integration to the network. This novel method based on estimation is proposed to determine the size of DG and its effects on the network to get rid of the coercive and time-consuming load flow techniques. Machine learning algorithms, such as Linear Regression, Artificial Neural Network, Support Vector Regression, K-Nearest Neighbor, and Decision Tree, have been used for the estimations and have been applied to well-known test systems, such as IEEE 12-bus, 33-bus, and 69-bus distribution systems. The accuracy of the proposed estimation methods has been verified with R-squared and mean absolute percentage error. Results show that the proposed DG allocation method is effective, applicable, and flexible.

scholarly journals Topology identification in distribution system via machine learning algorithms

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252436
Author(s):  
Peyman Razmi ◽  
Mahdi Ghaemi Asl ◽  
Giorgio Canarella ◽  
Afsaneh Sadat Emami
Keyword(s):  
Machine Learning ◽  
Distribution System ◽  
Distribution Systems ◽  
Learning Algorithms ◽  
Distribution Networks ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Voltage Profile ◽  
Topology Identification ◽  
Switching Devices

This paper contributes to the literature on topology identification (TI) in distribution networks and, in particular, on change detection in switching devices’ status. The lack of measurements in distribution networks compared to transmission networks is a notable challenge. In this paper, we propose an approach to topology identification (TI) of distribution systems based on supervised machine learning (SML) algorithms. This methodology is capable of analyzing the feeder’s voltage profile without requiring the utilization of sensors or any other extraneous measurement device. We show that machine learning algorithms can track the voltage profile’s behavior in each feeder, detect the status of switching devices, identify the distribution system’s typologies, reveal the kind of loads connected or disconnected in the system, and estimate their values. Results are demonstrated under the implementation of the ANSI case study.

scholarly journals A Study on the Development of Machine-Learning Based Load Transfer Detection Algorithm for Distribution Planning

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4358
Author(s):  
Jun-Hyeok Kim ◽  
Byung-Sung Lee ◽  
Chul-Hwan Kim
Keyword(s):  
Machine Learning ◽  
Distribution System ◽  
Distribution Systems ◽  
Load Transfer ◽  
Short Term Memory ◽  
Detection Algorithm ◽  
Quality Data ◽  
Load Prediction ◽  
Distribution Planning ◽  
Proposed Model

Distribution planning refers to the act of estimating the risks of distribution systems that may arise in the future and establishing investment plans to cope with them. Forecasted loads are one of the most typical variables used to analyze the risk of the distribution system, thus the efficiency of distribution planning may vary depending on its accuracy. For these reasons, a lot of studies are also being conducted to perform load prediction by incorporating the latest methods, such as machine learning (ML). However, the unchangeable fact is that no matter what prediction method is used, the accuracy and reliability of the predicted load can vary depending on the reliability of the data used. In particular, the detection of temporary load increases, due to load transfer that can occur frequently in the distribution system are essential for securing high-quality data. Therefore, in this study, a LSTM (Long Short-Term Memory) based load transfer detection model was proposed, and the appropriateness and reliability of the proposed method were analyzed by comparing actual planned load transfer data with the estimated load transfer results from the proposed model. It was also shown that the proposed model can improve the efficiency and reliability of the distribution planning by reasonably removing load variations, due to load transfer.

Unit Template Based Control of PV-DSTATCOM

2020 ◽  
Vol 13 (1) ◽  
pp. 36-42
Author(s):  
Gunjan Varshney ◽  
Durg S. Chauhan ◽  
Madhukar P. Dave ◽  
Nitin
Keyword(s):  
Power Quality ◽  
Power Factor ◽  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Phase Distribution ◽  
Voltage Regulation ◽  
Photovoltaic Array ◽  
Quality Issues ◽  
Static Compensator

Background: In modern electrical power distribution systems, Power Quality has become an important concern due to the escalating use of automatic, microprocessor and microcontroller based end user applications. Methods: In this paper, power quality improvement has done using Photovoltaic based Distribution Static Compensator (PV-DSTATCOM). Complete simulation modelling and control of Photovoltaic based Distribution Static Compensator have been provided in the presented paper. In this configuration, DSTATCOM is fed by solar photovoltaic array and PV module is also helpful to maintain the DC link voltage. The switching of PV-STATCOM is controlled by Unit template based control theory. Results: The performance of PV-DSTATCOM has been evaluated for Unity Power Factor (UPF) and AC Voltage Control (ACVC) modes. Here, for studying the power quality issues three-phase distribution system is considered and results have been verified through simulation based on MATLAB software. Conclusion: Different power quality issues and their improvement are studied and presented here for harmonic reduction, DC voltage regulation and power factor correction.

scholarly journals A Game-Theoretic Loss Allocation Approach in Power Distribution Systems with High Penetration of Distributed Generations

Mathematics ◽  
2018 ◽  
Vol 6 (9) ◽  
pp. 158
Author(s):  
Farzaneh Pourahmadi ◽  
Payman Dehghanian
Keyword(s):  
Power Systems ◽  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Power Distribution Systems ◽  
Loss Allocation ◽  
Distributed Generators ◽  
High Penetration ◽  
Game Theoretic ◽  
Allocation Approach

Allocation of the power losses to distributed generators and consumers has been a challenging concern for decades in restructured power systems. This paper proposes a promising approach for loss allocation in power distribution systems based on a cooperative concept of game-theory, named Shapley Value allocation. The proposed solution is a generic approach, applicable to both radial and meshed distribution systems as well as those with high penetration of renewables and DG units. With several different methods for distribution system loss allocation, the suggested method has been shown to be a straight-forward and efficient criterion for performance comparisons. The suggested loss allocation approach is numerically investigated, the results of which are presented for two distribution systems and its performance is compared with those obtained by other methodologies.

scholarly journals Application of genetic programming to modeling pipe failures in water distribution systems

2010 ◽  
Vol 13 (3) ◽  
pp. 419-428 ◽  
Author(s):  
Qiang Xu ◽  
Qiuwen Chen ◽  
Weifeng Li
Keyword(s):  
Genetic Programming ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Pipe Failure ◽  
Beijing City ◽  
Failure Data ◽  
Processing Strategies ◽  
Gis Data ◽  
Failure Models

The water loss from a water distribution system is a serious problem for many cities, which incurs enormous economic and social loss. However, the economic and human resource costs to exactly locate the leakage are extraordinarily high. Thus, reliable and robust pipe failure models are demanded to assess a pipe's propensity to fail. Beijing City was selected as the case study area and the pipe failure data for 19 years (1987–2005) were analyzed. Three different kinds of methods were applied to build pipe failure models. First, a statistical model was built, which discovered that the ages of leakage pipes followed the Weibull distribution. Then, two other models were developed using genetic programming (GP) with different data pre-processing strategies. The three models were compared thereafter and the best model was applied to assess the criticality of all the pipe segments of the entire water supply network in Beijing City based on GIS data.

scholarly journals Protection of Sensitive Loads in Distribution Systems Using a BSFCL-DVR System

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1615
Author(s):  
Mehdi Firouzi ◽  
Saleh Mobayen ◽  
Hossein Shahbabaei Kartijkolaie ◽  
Mojtaba Nasiri ◽  
Chih-Chiang Chen
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Fault Current ◽  
Dynamic Voltage Restorer ◽  
Voltage Quality ◽  
Voltage Restorer ◽  
Point Of Common Coupling ◽  
Dynamic Voltage ◽  
Bridge Type ◽  
In The Beginning

In this paper, an incorporated bridge-type superconducting fault current limiter (BSFCL) and Dynamic Voltage Restorer (DVR) is presented to improve the voltage quality and limiting fault current problems in distribution systems. In order to achieve these capabilities, the BSFCL and DVR are integrated through a common DC link as a BSFCL-DVR system. The FCL and DVR ports of the BSFCL-DVR system are located in the beginning and end of the sensitive loads’ feeder integrated to the point of common coupling (PCC) in the distribution system. At first, the principle operation of the BSFCL-DVR is discussed. Then, a control system for the BSFCL-DVR system is designed to enhance the voltage quality and limit the fault current. Eventually, the efficiency of the BSFCL-DVR system is verified through the PSCAD/EMTDC simulation.

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