IoT Based Non–Technical Loss Detection and Mitigation System for Power Distribution Networks

2021 ◽  
Vol 6 (7) ◽  
pp. 169-172
Author(s):  
Emmanuel M. Eronu ◽  
Matthew O. Oboh ◽  
Emeka S. Ezeh ◽  
Gafar Tiamiyu ◽  
Farouq E. Shaibu
Keyword(s):  
Regression Analysis ◽  
Power Distribution ◽  
Sensor Node ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Sensor Nodes ◽  
Current Sensor ◽  
Utility Company ◽  
The World ◽  
Self Powered

Electrical Energy crisis is a major problem faced in the world today and it’s increasingly significant in this part of Africa. A perfect solution seems not to be feasible as several solutions have been proposed in the past by various authors with little impact on the power sector. In this work, we present a method of Non-Technical Loss (NTL) detection consisting of a microcontroller interfaced with a current sensor that measures the current on the power line. A sensor node is placed at the supply end of the pole while two or more others sensor nodes are connected to the output of the pole depending on the number of consumers. The measured value of current is sent via the microcontroller to a web cloud that is accessible by the consumers and the utility company from any part of the world by simply logging on to the website; www.electricity-theft.herokuapp.com. The design uses the principle of Kirchhoff Current Law (KCL) to achieve this aim. The consumers can therefore monitor their power consumption from any location in the world and prevent theft on the network. The results obtained from the installation of the sensor nodes were analyzed using correlation and regression analysis. A correlation analysis of the data results gave us a correlation coefficient of 0.9802, while a regression analysis provided us with a linear relationship between the dependent and independent variable expressed mathematically thus Y = 0.916x + 0.254. A regression graph is also plotted. Furthermore, a T-Test and F-Test was conducted to statistically test the sensor nodes. A NodeMCU Wi-Fi microcontroller and a self-powered Phidget current sensor is used for the sensor node design. Communication between the sensor nodes is via Wi-Fi while a 4G router was used to provide internet services.

scholarly journals MACHINE LEARNING AND IOT FOR SMART GRID

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 233-240
Author(s):  
M. Fouad ◽  
R. Mali ◽  
A. Lmouatassime ◽  
M. Bousmah
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Grid ◽  
Power Distribution ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
Electricity Grid

Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.

scholarly journals A Novel Optimization Method for a Multi-Year Planning Scheme of an Active Distribution Network in a Large Planning Zone

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3450
Author(s):  
Xuejun Zheng ◽  
Shaorong Wang ◽  
Zia Ullah ◽  
Mengmeng Xiao ◽  
Chang Ye ◽  
...  
Keyword(s):  
Power Distribution ◽  
Large Scale ◽  
Distribution Network ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Optimization Method ◽  
Scheme Optimization ◽  
Optimization Task ◽  
Novel Approach ◽  
Planning Scheme

Electric power distribution networks plays a significant role in providing continuous electrical energy to different categories of customers. In the context of the present advancements, future load expansion in the active distribution networks (ADNs) poses the key challenge of planning to be derived as a multi-stage optimization task, including the optimal expansion planning scheme optimization (EPSO). The planning scheme optimization is a multi-attribute decision-making issue with high complexity and solving difficulty, especially when it involves a large-scale planning zone. This paper proposes a novel approach of a multi-year planning scheme for the effective solution of the EPSO problem in large planning zones. The proposed approach comprises three key parts, where the first part covers two essential aspects, i.e., (i) suggesting a project condition set that considers the elements directly related to a group of specific conditions and requirements (collectively referred to as conditions) to ADN planning projects; and (ii) Developing a condition scoring system to evaluate planning projects. The second part of our proposed scheme is a quantization method of correlativity among projects based on two new concepts: contribution index (CI) and dependence index (DI). Finally, considering the multi-year rolling optimization, a detailed mathematical model of condition evaluation and spatiotemporal optimization sequencing of ADN planning projects is developed, where the evaluation and optimization are updated annually. The proposed model has been successfully validated on a practical distribution network located in Xiantao, China. The investigated case study and comparisons verify the various advantages, suitability, and effectiveness of the proposed planning scheme, consequently saving more than 10% of the investment compared with the existing implemented scheme.

Thermoelectric Powered Autonomous Wireless Sensor Module for Temperature Monitoring

2011 ◽  
Vol 63-64 ◽  
pp. 978-982 ◽  
Author(s):  
Wen Si Wang ◽  
Ning Ning Wang ◽  
Michael Hayes ◽  
Brendan O'Flynn ◽  
Cian O'Mathuna
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Energy Harvester ◽  
Electrical Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Battery Life ◽  
Wireless Sensor Node ◽  
Battery Lifetime ◽  
Sensor Module

Wireless sensor networks are frequently used to monitor temperature and other manufacturing parameters in recent years. However, the limited battery life posts a constraint for large sensor networks. In this work, thermoelectric energy harvester is designed to effectively convert the heat into electrical energy to power the wireless sensor node. Bismuth telluride thermoelectric modules are optimized for low temperature conditions. Charge pump and switching regulator based power management module is designed to efficiently step up the 500mV thermoelectric voltage to 3.0V level for wireless sensor nodes. This design employs electric double-layer capacitor based energy storage with considerations on practical wireless sensor node operation. The implemented energy harvester prototype is proposed for Tyndall wireless sensor system to monitor temperature and relative humidity in manufacturing process. The prototype was tested in various conditions to discover the issues in this practical design. The proposed prototype can expect a 15 years operative lifetime instead of the 3-6 months battery lifetime.

scholarly journals A Novel Charging Method for Underwater Batteryless Sensor Node Networks

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 557
Author(s):  
Judith Santana Abril ◽  
Graciela Santana Sosa ◽  
Javier Sosa ◽  
Tomas Bautista ◽  
Juan A. Montiel-Nelson
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Hardware Implementation ◽  
Traditional Approach ◽  
Sensor Nodes ◽  
Current Sensor ◽  
Wireless Power ◽  
Fish Farms ◽  
Wireless Power Transfer System ◽  
New Feature

In this paper, we present a novel charging method for underwater batteryless sensor node networks. The target application is a practical underwater sensor network for oceanic fish farms. The underwater sections of the network use a wireless power transfer system based on the ISO 11784/11785 HDX standard for supplying energy to the batteryless sensor nodes. Each sensor has an accumulator capacitor, which is charged for voltage supplying to the sensor node. A new distributed charging scheme is proposed and discussed in detail to reduce the required time to charge all sensor nodes of the underwater sections. One important key is its decentralized control of the charging process. The proposal is based on the self disconnection ability of each sensor node from the charging network. The second important key is that the hardware implementation of this new feature is quite simple and only requires to include a minimal circuitry in parallel to the current sensor node antenna while the rest of the sensor network remains unaltered. The proposed charging scheme is evaluated using real corner cases from practical oceanic fish farms sensor networks. The results from experiments demonstrate that it is possible to charge up to 10 sensor nodes which is the double charging capability than previous research presented. In the same conditions as the approach found in the literature, it represents reaching an ocean depth of 60 m. In terms of energy, in case of an underwater network with 5 sensors to reach 30 m deep, the proposed charging scheme requires only a 25% of the power required using the traditional approach.

scholarly journals A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3137
Author(s):  
Vytautas Ostasevicius ◽  
Paulius Karpavicius ◽  
Agne Paulauskaite-Taraseviciene ◽  
Vytautas Jurenas ◽  
Arkadiusz Mystkowski ◽  
...  
Keyword(s):  
Sensor Node ◽  
Electrical Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Wireless Sensor Node ◽  
End Mill ◽  
Machine Learning Approach ◽  
Wear Monitoring ◽  
Electrical Impulses

There are many tool condition monitoring solutions that use a variety of sensors. This paper presents a self-powering wireless sensor node for shank-type rotating tools and a method for real-time end mill wear monitoring. The novelty of the developed and patented sensor node is that the longitudinal oscillations, which directly affect the intensity of the energy harvesting, are significantly intensified due to the helical grooves cut onto the conical surface of the tool holder horn. A wireless transmission of electrical impulses from the capacitor is proposed, where the collected electrical energy is charged and discharged when a defined potential is reached. The frequency of the discharge pulses is directly proportional to the wear level of the tool and, at the same time, to the surface roughness of the workpiece. By employing these measures, we investigate the support vector machine (SVM) approach for wear level prediction.

A Fractal-Inspired Multi-Frequency Piezoelectric Energy Converter: Computational and Experimental Characterization

2012 ◽  
Author(s):  
Davide Castagnetti
Keyword(s):  
Power Output ◽  
Piezoelectric Materials ◽  
Electrical Energy ◽  
Sensor Nodes ◽  
Energy Converter ◽  
Piezoelectric Energy ◽  
Physical Prototype ◽  
Self Powered ◽  
Resistive Loads ◽  
Energy Harvesting Devices

In order to develop self-powered wireless sensor nodes, many energy harvesting devices, able to convert freely available ambient energy into electrical energy, have been proposed in the literature. A promising technique, in terms of simplicity and high conversion efficiency, is the harvesting of ambient kinetic energy through piezoelectric materials. The aim of this work is to design and investigate the modal response and the power output of a fractal-inspired, multi-frequency, piezoelectric energy converter, previously presented by the author. Two are the steps of the work. First, a computational modal analysis of the converter is performed. Second, a physical prototype of the converter is built and its eigenfrequencies and power generation under different resistive loads are experimentally examined in the range between 0 and 120 Hz. The converter exhibits three eigenfrequencies and a good power output, in particular at the first eigenfrequency.

scholarly journals Application of fuzzy regression analysis method for determination of electric power losses in intrafactory power supply networks

2019 ◽  
Vol 11 (4) ◽  
pp. 325-331 ◽  
Author(s):  
E. I. Gracheva ◽  
O. V. Naumov
Keyword(s):  
Regression Analysis ◽  
Electric Power ◽  
Power Supply ◽  
Power Distribution ◽  
Distribution Networks ◽  
Fuzzy Regression ◽  
Power Losses ◽  
Initial Information ◽  
Fuzzy Regression Analysis ◽  
The Cost

One of the main objectives of the development of modern industry in Russia, along with an increase in the absolute volumes of electric power (EP) production, is to strengthen control over its more rational use. Saving EP and reducing the cost of its transmission along power distribution networks is of great importance for the country's energy sector. In terms of their physical nature, in terms of production, transmission and consumption, EP losses are no different from EP served to consumers. Therefore, the assessment of power losses in electrical networks is based on the same economic principles as the assessment of energy served to consumers. EP losses have a significant impact on the technical and economic parameters of the network, since the cost of losses is included in the estimated cost (reduced costs) and cost price (annual operating costs) of EP transmission. The cost component of losses in the cost of EP transmission has a large proportion. The article presents the results of research on the possibility of application of fuzzy regression analysis for problems of assessment and prediction of electric power losses in intrafactory networks. Initial information on the network is uncertain to some extent, which complicates application of traditional methods. The calculation is presented for conventional and fuzzy regression models, along with estimation of error of these models. The relevance of application of fuzzy regression analysis methods is determined by the difficulty of obtaining reliable information about the circuit and regime parameters of intrafactory networks, the probabilistic nature of change of the modes, as well as a whole complex of affecting factors, which are generally challenging for quantitative assessment. Advantages of application of fuzzy regression analysis consist in obtaining confidence intervals of required variables (value of electric power losses) for schemes of networks with uncertain initial information on their parameters, which is characteristic of intrafactory power supply systems, and enables to consider dynamics of their variation.

scholarly journals Monitoring Penggunaan Daya listrik Sebagai Implementasi Internet of Things Berbasis Wireless Sensor Network

2017 ◽  
Vol 16 (3) ◽  
pp. 50
Author(s):  
I Gusti Putu Mastawan Eka Putra ◽  
Ida Ayu Dwi Giriantari ◽  
Lie Jasa
Keyword(s):  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Sensor Network ◽  
Sensor Node ◽  
Electrical Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
The Internet ◽  
Wireless Sensor Node ◽  
Electrical Parameters

One implementation of the Internet of Things (IoT) conducted in this study to realize the system of monitoring and control of electrical energy usage-based Wireless Sensor Network (WSN). This research method is the design of wireless sensor nodes that can measure the electrical parameters of alternating current (AC) as effective voltage, effective current, active power, apparent power, power factor and total electrical energy consumption by using modules ESP8266 as a liaison with a Wi-Fi. Calculation of electrical parameters obtained from ATmega328P microcontroller ADC readings of a step-down transformer that is used as a voltage sensor and sensor SCT013 used as AC current sensors will be transmitted to the server over the network from a Wi-Fi Access Point (AP). ESP8266 modules are programmed using AT-Command proven to reliably measure can transmit data simultaneously with serial data format of the wireless sensor node to a server using TCP / IP protocol. Monitoring power consumption via the internet which are designed in the research, either through the Android application and web browser proven to be reliably able to show some electrical parameters with the same data than the data logger recaps taken from SD-Card installed in the wireless sensor node.

On Energy Status indicators and the role of renewableEnergy under Economic Crisis

2014 ◽  
pp. 92-105
Author(s):  
P. Bezrukikh ◽  
P. Bezrukikh (Jr.)
Keyword(s):  
Renewable Energy ◽  
World Economy ◽  
Electrical Energy ◽  
Primary Energy ◽  
Primary Energy Consumption ◽  
Energy Status ◽  
Economy Growth ◽  
The World ◽  
Production Regime

The article analyzes the dynamics of consumption of primary energy and production of electrical energy in the world for 1973-2012 and the volume of renewable energy. It is shown that in the crisis year of 20 0 9 there was a significant reduction in primary energy consumption and production of electrical energy. At the same time, renewable energy has developed rapidly, well above the rate of the world economy growth. The development of renewable energy is one of the most effective ways out of the crisis, taking into account its production regime, energy, environmental, social and economic efficiency. The forecast for the development of renewable energy for the period up to 2020, compiled by the IEA, is analyzed. It is shown that its assessment rates are conservative; the authors justify higher rates of development of renewable energy.

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