scholarly journals Toward fault tolerant modelling for SCADA based electricity distribution networks, machine learning approach

2021 ◽  
Vol 7 ◽  
pp. e554
Author(s):  
Aladdin Masri ◽  
Muhannad Al-Jabi
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Smart Grids ◽  
Fault Tolerant ◽  
Distribution Networks ◽  
Minimum Time ◽  
Electricity Distribution ◽  
Modeling Tools ◽  
Load Redistribution ◽  
Modeling Techniques

Maintaining electrical energy is a crucial issue, especially in developing countries with very limited possibilities and recourses. However, the increasing reliance on electrical appliances generates many challenges for operators to fix any fault optimally within minimum time. Even with numerous researches conducted in this area, very few were interested in minimizing the fault duration, especially in the developing countries with very limited resources. Since decision-making requires enough information within minimum time, the integration of information technology with the existing electrical grids is the most appropriate. In this paper, we propose precise and accurate load redistribution estimation models. While several modeling techniques exist, the proposed modeling techniques in this work are based on machine learning models: multiple linear regression, nonlinear regression, and classifier neural network models. The novelty of this work is it introduces a fault-tolerant approach that relies on machine learning and supervisory control and data acquisition system (SCADA). The purpose of this approach is to help electricity distribution companies to maintain power for the customers and to shorten the fault duration from many hours to the minimum possible time. The work was performed based on real data of smart grids split into zones of about 20 transformers. The models’ input data collected from the sensors allocated in the power grid, make the grid becomes able to redistribute the loads by sufficient strategies. To test and validate the models, two powerful modeling tools were used: MATLAB and Anaconda–Python. The results showed an accuracy of about 97% with a standard deviation of 2.3%. The load redistribution was also presented in details. With such eager results, they approve the validity of our model in minimizing the fault duration, by helping the system in taking ideal actions within the optimal time.

scholarly journals Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1153 ◽  
Author(s):  
Mónica Marcela Giraldo-González ◽  
Juan Pablo Rodríguez
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Statistical Models ◽  
Water Distribution ◽  
Distribution Networks ◽  
Support Vector ◽  
Failure Rates ◽  
Learning Models ◽  
Pipe Failure ◽  
Machine Learning Models

The application of statistical and Machine Learning models plays a critical role in planning and decision support processes for efficient and reliable Water Distribution Network (WDN) management. Failure models can provide valuable information for prioritizing system rehabilitation even in data scarcity scenarios, such as developing countries. Few studies have analyzed the performance of more than two models, and examples of case studies in developing countries are insufficient. This study compares various statistical and Machine Learning models to provide useful information to practitioners for the selection of a suitable pipe failure model according to information availability and network characteristics. Three statistical models (i.e., Linear, Poisson, and Evolutionary Polynomial Regressions) were used for failure prediction in groups of pipes. Machine Learning approaches, particularly Gradient-Boosted Tree (GBT), Bayes, Support Vector Machines and Artificial Neuronal Networks (ANNs), were compared in predicting individual pipe failure rates. The proposed approach was applied to a WDN in Bogotá (Colombia). The statistical models showed an acceptable performance (R2 between 0.695 and 0.927), but the Poisson Regression was the most suitable for predicting failures in pipes with lower failure rates. Regarding Machine Learning models, Bayes and ANNs exhibited low performance in the prediction of pipe failure condition. The GBT approach had the best performing classifier.

scholarly journals Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4239 ◽  
Author(s):  
Ivanov ◽  
Neagu ◽  
Grigoras ◽  
Gavrilas
Keyword(s):  
Optimization Algorithm ◽  
Smart Grids ◽  
Reactive Power ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Metaheuristic Algorithms ◽  
System Level ◽  
Electricity Distribution ◽  
Capacitor Banks ◽  
Whale Optimization

Energy losses and bus voltage levels are key parameters in the operation of electricity distribution networks (EDN), in traditional operating conditions or in modern microgrids with renewable and distributed generation sources. Smart grids are set to bring hardware and software tools to improve the operation of electrical networks, using state-of the art demand management at home or system level and advanced network reconfiguration tools. However, for economic reasons, many network operators will still have to resort to low-cost management solutions, such as bus reactive power compensation using optimally placed capacitor banks. This paper approaches the problem of power and energy loss minimization by optimal allocation of capacitor banks (CB) in medium voltage (MV) EDN buses. A comparison is made between five metaheuristic algorithms used for this purpose: the well-established Genetic Algorithm (GA); Particle Swarm Optimization (PSO); and three newer metaheuristics, the Bat Optimization Algorithm (BOA), the Whale Optimization Algorithm (WOA) and the Sperm-Whale Algorithm (SWA). The algorithms are tested on the IEEE 33-bus system and on a real 215-bus EDN from Romania. The newest SWA algorithm gives the best results, for both test systems.

scholarly journals Smart electricity distribution networks, business models, and application for developing countries

Energy Policy ◽  
2018 ◽  
Vol 114 ◽  
pp. 22-29 ◽  
Author(s):  
Tooraj Jamasb ◽  
Tripta Thakur ◽  
Baidyanath Bag
Keyword(s):  
Developing Countries ◽  
Business Models ◽  
Distribution Networks ◽  
Electricity Distribution

scholarly journals A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1220
Author(s):  
Ovidiu Ivanov ◽  
Samiran Chattopadhyay ◽  
Soumya Banerjee ◽  
Bogdan-Constantin Neagu ◽  
Gheorghe Grigoras ◽  
...  
Keyword(s):  
Demand Response ◽  
Distribution System ◽  
Smart Grids ◽  
Low Voltage ◽  
Distribution Networks ◽  
Load Flow ◽  
Electricity Distribution ◽  
Energy Management Systems ◽  
Home Energy Management ◽  
Novel Algorithm

Demand Side Management (DSM) is becoming necessary in residential electricity distribution networks where local electricity trading is implemented. Amongst the DSM tools, Demand Response (DR) is used to engage the consumers in the market by voluntary disconnection of high consumption receptors at peak demand hours. As a part of the transition to Smart Grids, there is a high interest in DR applications for residential consumers connected in intelligent grids which allow remote controlling of receptors by electricity distribution system operators and Home Energy Management Systems (HEMS) at consumer homes. This paper proposes a novel algorithm for multi-objective DR optimization in low voltage distribution networks with unbalanced loads, that takes into account individual consumer comfort settings and several technical objectives for the network operator. Phase load balancing, two approaches for minimum comfort disturbance of consumers and two alternatives for network loss reduction are proposed as objectives for DR. An original and faster method of replacing load flow calculations in the evaluation of the feasible solutions is proposed. A case study demonstrates the capabilities of the algorithm.

scholarly journals Evolution of the Electricity Distribution Networks—Active Management Architecture Schemes and Microgrid Control Functionalities

2021 ◽  
Vol 11 (6) ◽  
pp. 2793
Author(s):  
Katja H. Sirviö ◽  
Hannu Laaksonen ◽  
Kimmo Kauhaniemi ◽  
Nikos Hatziargyriou
Keyword(s):  
Smart Grids ◽  
Low Voltage ◽  
Unified Modeling Language ◽  
Distribution Networks ◽  
Active Management ◽  
Modeling Language ◽  
Electricity Distribution ◽  
Management Architecture ◽  
Network Operations ◽  
Microgrid Control

The power system transition to smart grids brings challenges to electricity distribution network development since it involves several stakeholders and actors whose needs must be met to be successful for the electricity network upgrade. The technological challenges arise mainly from the various distributed energy resources (DERs) integration and use and network optimization and security. End-customers play a central role in future network operations. Understanding the network’s evolution through possible network operational scenarios could create a dedicated and reliable roadmap for the various stakeholders’ use. This paper presents a method to develop the evolving operational scenarios and related management schemes, including microgrid control functionalities, and analyzes the evolution of electricity distribution networks considering medium and low voltage grids. The analysis consists of the dynamic descriptions of network operations and the static illustrations of the relationships among classified actors. The method and analysis use an object-oriented and standardized software modeling language, the unified modeling language (UML). Operational descriptions for the four evolution phases of electricity distribution networks are defined and analyzed by Enterprise Architect, a UML tool. This analysis is followed by the active management architecture schemes with the microgrid control functionalities. The graphical models and analysis generated can be used for scenario building in roadmap development, real-time simulations, and management system development. The developed method, presented with high-level use cases (HL-UCs), can be further used to develop and analyze several parallel running control algorithms for DERs providing ancillary services (ASs) in the evolving electricity distribution networks.

Protection of Electricity Distribution Networks

2011 ◽  
Author(s):  
Juan M. Gers ◽  
Edward J. Holmes
Keyword(s):  
Distribution Networks ◽  
Electricity Distribution

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Column-Generation for Design of Survivable Electricity Distribution Networks

2004 ◽  
Author(s):  
Kavinesh Singh ◽  
Andy Philpott ◽  
Kevin Wood
Keyword(s):  
Column Generation ◽  
Distribution Networks ◽  
Electricity Distribution

Critical Review of Data Analytics techniques used in the Expanded Program on Immunization (EPI)

2020 ◽  
Vol 16 ◽  
Author(s):  
Sadaf Qazi ◽  
Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Data Analytics ◽  
Public Health Intervention ◽  
Cold Chain ◽  
Data Repository ◽  
Quality Of Data ◽  
Mortality And Morbidity ◽  
Healthcare Facilities ◽  
Expanded Program On Immunization

Background: Immunization is a significant public health intervention to reduce child mortality and morbidity. However, its coverage, in spite of free accessibility, is still very low in developing countries. One of the primary reasons for this low coverage is the lack of analysis and proper utilization of immunization data at various healthcare facilities. Purpose: In this paper, the existing machine learning based data analytics techniques have been reviewed critically to highlight the gaps where this high potential data could be exploited in a meaningful manner. Results: It has been revealed from our review, that the existing approaches use data analytics techniques without considering the complete complexity of Expanded Program on Immunization which includes the maintenance of cold chain systems, proper distribution of vaccine and quality of data captured at various healthcare facilities. Moreover, in developing countries, there is no centralized data repository where all data related to immunization is being gathered to perform analytics at various levels of granularities. Conclusion: We believe that the existing non-centralized immunization data with the right set of machine learning and Artificial Intelligence based techniques will not only improve the vaccination coverage but will also help in predicting the future trends and patterns of its coverage at different geographical locations.

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