Regression based approach for measurement of current in single-phase smart energy meter

Singh, Shobhana; Arun, S. L.; Selvan, M. P.

doi:10.1109/tenconspring.2017.8070103

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…The sensor is a low-cost, Hall-effect based linear current sensor with 2.1 KV Rms isolation with a low-resistant current conductor [13]. The sensor allows up to 5 V of power supply [22]. The magnetic field inside the sensor is converted to a proportional voltage by the hall IC, which is fused into the sensor system.…”

Section: Acs 712 Hall-effect Current Sensormentioning

confidence: 99%

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An Open Source LoRa Based, Low-Cost IoT Platform for Renewable Energy Generation Unit Monitoring and Supervisory Control

Ndukwe¹,

Iqbal²,

Khan³

2021

JEPT

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SCADA provides real-time system monitoring by constant communication and data exchange between various system devices to achieve data visualization and logging. Presently, in industrial systems, commercial SCADA systems are being used for data monitoring and control. These systems can be expensive, and as such can only be afforded by select industries. Even at these costs, the commercial SCADA systems face some challenges, which include interoperability and scalability issues. Research has shown that these problems can be solved by the introduction of low-cost materials and open-source software to achieve data monitoring for all levels of processes. This paper proposes an open source, low-cost Internet of Things (IoT)-based SCADA system that employs the IoT architecture for SCADA functions. The proposed system is an improvement to the existing IoT solutions by eliminating cloud based IoT platforms and introducing a single machine system. This solution increases the robustness of the system while reducing costs. The proposed system prototype consists of voltage and current sensors, Arduino Uno microcontroller and Raspberry Pi. The sensors acquire data from the monitored unit. The Arduino Uno receives the data and processes them for transmission to the Raspberry Pi using the LoRa communication technology. At the Raspberry Pi, the local Chirpstack platform processes the data and displays the measured data using the Grafana dashboard for real-time data monitoring, and the data is stored in an InfluxDB database. For system validation purposes, the prototype is designed, developed, and set up to monitor the panel voltage, current and battery voltage of a solar photovoltaic system. The results obtained from the experimental set-up are compared with the test data from physical digital multimeters. The system presented in this paper is a low-cost, open source, scalable and interoperable system. This, therefore, makes the proposed SCADA system an alternative for commercial SCADA systems, especially for select applications. The system proposed in this paper can be deployed to large industrial systems with appropriate upgrades and customization. The main contribution of this research is the design and development of a SCADA system that performs all the functions of a proprietary SCADA system at a very low-cost with scalable and interoperability features which are the main limitations of the traditional SCADA systems.

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Section: Acs 712 Hall-effect Current Sensormentioning

confidence: 99%

“…The magnetic field inside the sensor is converted to a proportional voltage by the hall IC, which is fused into the sensor system. The voltage obtained at the output is proportional to the measured current [22]. Three classes of the cur-rent sensor (30A, 20A and X5B) were employed for this work.…”

Section: Acs 712 Hall-effect Current Sensormentioning

confidence: 99%

An Open Source LoRa Based, Low-Cost IoT Platform for Renewable Energy Generation Unit Monitoring and Supervisory Control

Ndukwe¹,

Iqbal²,

Khan³

2021

JEPT

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“…Demand response (DR) and HEMS are described in [5]. It conveys that HEMSs along with smart energy meters [6] will bring DR on a wide scale. IEEE 802.15.4 and ZigBee based HAN is developed by Han and Lim, which provides light control and multi-sensing design.…”

Section: Smart Load Node: What Is It and Why Is It Neededmentioning

confidence: 99%

Smart Load Node for Nonsmart Load Under Smart Grid Paradigm: A New Home Energy Management System

Singh

Roy

Selvan

2019

IEEE Consumer Electron. Mag.

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This article presents a novel approach for efficient operation of non-smart household appliances under smart grid environment using the proposed smart load node (SLN). In real world scenario, there are so many non-smart loads currently in use and embedding appliance specific intelligence into them to make them as smart loads will be more expensive compared to the proposed SLN, which is a common solution for all types of nonsmart loads. This makes the proposed low-cost SLN, which neither requires any infrastructural change in the electrical wiring of a house nor any constructional change in home appliances at the manufacturing stage and at the consumer end, as a feasible solution for intelligent operation of nonsmart home appliances under smart grid environment. The SLNs, which are placed in a home like distributed wireless sensor nodes, form a home area network (HAN). The HAN includes a load management unit (LMU) which acts as master for all distributed SLNs. Wi-Fi is chosen as a medium of communication in the HAN. The LMU incorporates load management algorithm which is written in Python script.

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“…Contudo, o uso mais comum para os dispositivos IoT, devidoàs restrições de memória, energia e processamento,é como coletores de dados, como [4], [5], [6], [7], sendo que todo o processamento e tomada de decisão ocorre fora dos dispositivos [8], [9]. Porém, nem sempreé possível transmitir os dados coletados para processamento, pois não há conectividade, há limitações de energia que dificultam a transmissão, problemas relacionadosà latência eà privacidade dos dados, tudo isto com a necessidade ou urgência deste processamento dos dados coletados.…”

Section: Introductionunclassified

“…O algoritmo de regressão linear polinomialé utilizado para prever valores e comportamentos a partir da análise estatística de dados.É uma técnica que envolve normalmente um conjunto de dados ordenados e que gera, ao final, os coeficientes de uma equação polinomial e seus erros estatísticos a partir de cálculos matriciais. Normalmente, o treinamento ou geração dos coeficientes do polinômioé feito em uma máquina com um poder de processamento maior, para a obtenção dos valores de erro e posteriormente,é feita a inserção da função obtida pelo algoritmo no código implementado no hardware de baixo custo [4], [13]. Já quando a solução do algoritmoé calculada diretamente no hardware, a sua utilizaçãoé para uma solução de regressão linear simples [14], [15].…”

Section: Introductionunclassified

Análise de Requisitos Computacionais de Técnicas de Aprendizado de Máquina para Implementação em Hardwares de Baixo Custo

Júnior¹,

Loiola²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Resumo-Com o aumento da popularidade dos hardwares de baixo custo, como os dispositivos embarcados, e o crescente uso dos algoritmos de aprendizagem de máquina, há uma demanda para que estes trabalhem nas mais diversasáreas, pois nem sempre há a possibilidade de uma comunicação dos dispositivos com os hardwares mais robustos ondeé executado normalmente o algoritmo. Portanto, este trabalho vem propor uma avaliação dos algoritmos de regressão linear polinomial e rede neural Perceptron multicamada, efetuando uma análise de requisitos computacionais para uso em hardwares de baixo custo.

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Regression based approach for measurement of current in single-phase smart energy meter

Cited by 7 publications

References 15 publications

An Open Source LoRa Based, Low-Cost IoT Platform for Renewable Energy Generation Unit Monitoring and Supervisory Control

An Open Source LoRa Based, Low-Cost IoT Platform for Renewable Energy Generation Unit Monitoring and Supervisory Control

Smart Load Node for Nonsmart Load Under Smart Grid Paradigm: A New Home Energy Management System

Análise de Requisitos Computacionais de Técnicas de Aprendizado de Máquina para Implementação em Hardwares de Baixo Custo

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