Distribution System State Estimation to Support Coordinated Voltage-Control Strategies by Using Smart Meters

Alzate, Efrain Bernal; López, Manuel Gómez; Xie, Junhao; Strunz, Kai

doi:10.1109/tpwrs.2019.2902184

Cited by 40 publications

(10 citation statements)

References 43 publications

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“…) in which n is the iteration index, x n is the state vector in iteration n, and G(x) is the gain matrix, there is only one error vector in which the error of each measured parameter is considered a constant value [6,12,13]. Examining the time standards in the meters at the network indicates that the minimum time for each data sampling and sending is 15 min in most cases.…”

Section: Case Studymentioning

confidence: 99%

Improving state estimation accuracy in active distribution networks by coordinating real‐time and pseudo‐measurements considering load uncertainty

Ajoudani

Shiekholeslami

Zakariazadeh

2022

IET Generation Trans & Dist

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Measurements at the level of active distribution networks can be classified into three major groups. Data collection and transmission involve the time interval of several seconds for the first category, several minutes to 24 h for the second category, and finally, several weeks to several months for the third category. Considering this three-level classification of the measurements, state estimation by these data should deal with three kinds of heterogeneous data, but it has undesirable effects on the state estimation process, such as lower accuracy of the results. Despite the existence of heterogeneous data and different time rates, this study presents a state estimation model for updating measurement devices in each of the categories, aiming at establishing coordination among the measured data and increasing the accuracy of the estimation. The opportunities and limitations of the proposed model are compared to those of the conventional models. To illustrate the effectiveness of the proposed method, two IEEE standard systems 34-bus and 123-bus, have been used. The experimental network includes various load patterns, as well as distributed generation sources whose uncertainty is considered in the proposed model. 1Various measurement devices are currently installed on distribution networks. They are compared in Table 1.

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Section: Case Studymentioning

confidence: 99%

Improving state estimation accuracy in active distribution networks by coordinating real‐time and pseudo‐measurements considering load uncertainty

Ajoudani

Shiekholeslami

Zakariazadeh

2022

IET Generation Trans & Dist

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“…The proposed ISHDB system is not only technically sound, but it is also cost-effective for the household applications. Despite several research papers published in the literature on the IoT-based smart HEMS [6,7,26,40,41], a detailed investigation of ISHDB prototype accompanied by an estimation of the implementation cost is barely provided. To estimate the cost of the working prototype precisely, several factors such as features of the used components, number of the electricity lines, and the nature and number of loads that it can manage have to be considered.…”

Section: Cost Estimationmentioning

confidence: 99%

“…For instance, renewable energy-based smart home energy management system (HEMS) is presented in [6] to optimize the power generation and consumption. In [7], an internet of things (IoT)-related smart meter is explored to support voltage control strategies within the low voltage distribution system of 30 smart homes. Although the simulation analysis shows good performance, the proposed methodology can be only used to improve the user-friendly conditions.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective Design of IoT-Based Smart Household Distribution System

Ahmed

Qays

Abu-Siada

et al. 2021

Designs

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The Internet of Things (IoT) plays an indispensable role in present-day household electricity management. Nevertheless, practical development of cost-effective intelligent condition monitoring, protection, and control techniques for household distribution systems is still a challenging task. This paper is taking one step forward into a practical implementation of such techniques by developing an IoT Smart Household Distribution Board (ISHDB) to monitor and control various household smart appliances. The main function of the developed ISHDB is collecting and storing voltage, current, and power data and presenting them in a user-friendly way. The performance of the developed system is investigated under various residential electrical loads of different energy consumption profiles. In this regard, an Arduino-based working prototype is employed to gather the collected data into the ThingSpeak cloud through a Wi-Fi medium. Blynk mobile application is also implemented to facilitate real-time monitoring by individual consumers. Microprocessor technology is adopted to automate the process, and reduce hardware size and cost. Experimental results show that the developed system can be used effectively for real-time home energy management. It can also be used to detect any abnormal performance of the electrical appliances in real-time through monitoring their individual current and voltage waveforms. A comparison of the developed system and other existing techniques reveals the superiority of the proposed method in terms of the implementation cost and execution time.

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“…The comparative study among DSSE's based on accuracy, computational burden, complexity, mean absolute percentage error (MAPE), absolute value error etc. are summarised in Table 2 from available literatures [62,67,[76][77][78][79][80] for proper selection and modification of DSSE algorithm.…”

Section: Comparative Study For Various Dssementioning

confidence: 99%

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

Bhattar

Pindoriya

Sharma

2021

IET cyber-phys. syst.

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The penetration of renewable energy sources (RES) in distribution systems has entailed heavy deployment of monitoring and control infrastructure in the distribution system. This deployment of sensors and communication infrastructure has led to the emergence of a modern grid with a complex cyber-physical network. On one hand, the cyber-physical system can play a pivotal role in coordination and control of distribution systems through distribution system state estimation (DSSE). On the other hand, the risk caused by cyber-attack has emerged as a major challenge for grid operation. The modelling aspects, namely DC loads, plug-in-vehicle and distributed generators (DGs) in DSSE and security aspects in cyber-physical distribution systems, are highlighted herein. This paper is divided into different sections emphasising on the problems and challenges associated with DSSE, cyber-attack focussing primarily on impact of false data injection (FDI) attack and co-simulation platform for investigation of vulnerability of cyber-physical systems. A comparative study among various DSSE and challenges namely, pseudo-measurement, time synchronisation and communication issues are discussed. The potential investigation of FDI and need for a co-simulation platform in distribution systems for risk analysis are also addressed. The opportunities and future research in the field of cyber-physical distribution systems are discussed in depth. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Distribution System State Estimation to Support Coordinated Voltage-Control Strategies by Using Smart Meters

Cited by 40 publications

References 43 publications

Improving state estimation accuracy in active distribution networks by coordinating real‐time and pseudo‐measurements considering load uncertainty

Improving state estimation accuracy in active distribution networks by coordinating real‐time and pseudo‐measurements considering load uncertainty

Cost-Effective Design of IoT-Based Smart Household Distribution System

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

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