Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting

Song, Shaojian; Lin, Yuzhang; Huang, Qingbao; Lin, Xin; Wang, Fei

doi:10.1016/j.epsr.2019.105974

Cited by 30 publications

(19 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The accuracy of pseudo-measurements poses challenge for error-free estimation. The forecasting-based model are adopted for depending on unmonitored DG's namely, machine learning-based algorithm with evolutionary algorithms, forecasting model [44]. The stochastic variation of load demand, uncertainty in power generation and presence of various noise levels in telemetered data in DG necessitate the development of appropriate forecasting model for DSSE [45].…”

Section: Dg In 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.

View full text Add to dashboard Cite

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.

show abstract

Section: Dg In 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.

View full text Add to dashboard Cite

show abstract

“…Several authors proposed artificial neural networks (ANN)-based DSSE algorithms to overcome a limited number of measurements in distribution networks [13,14]. Cheng et al [15] combined extreme machine learning with a genetic algorithm to predict the photovoltaic generation and used it as a pseudo-measurement in a WLS-based DSSE algorithm. Considering the problem of SE as a global optimization problem with constraints, some authors have suggested metaheuristic-based methods for solving it.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Novel PSOS-CGSA Method for State Estimation in Unbalanced DG-Integrated Distribution Systems

et al. 2020

View full text Add to dashboard Cite

This paper proposes a novel optimization method, namely, hybrid PSOS-CGSA for state estimation in three-phase unbalanced DG-integrated distribution systems. The distribution system state estimation (DSSE) is formulated as a nonlinear optimization problem with constraints where loads and DG outputs are considered as the control variables, while real-time measurements are treated as dependent variables. The proposed DSSE model estimates the loads and DG outputs at each bus by minimizing the difference between the measure and calculated values of the variables monitored in real-time. A novel hybrid algorithm of particle swarm optimization with sigmoid-based acceleration coefficients and chaotic gravitational search algorithm (PSOS-CGSA) is proposed and applied for the DG-integrated DSSE. The feasibility of the proposed approach is verified on the IEEE 13-bus test system, the IEEE 37-bus test system, and the IEEE 123-bus test system. These simulations show that the proposed DSSE model provides reliable and accurate state estimation of DG-integrated distribution systems with a very limited number of real-time measurements at the source substation. The results obtained by proposed hybrid PSOS-CGSA are evaluated by comparing with other methods under the same test conditions, and the obtained results demonstrate the merits of the proposed scheme. INDEX TERMS Hybrid intelligent algorithm, metaheuristic optimization, state estimation, unbalanced distribution system. Zia Ullah received the Bachelor degree in electrical power from University of Engineering and Technology, Lahore Pakistan in 2007, and the MS degree in electrical engineering from COMSATS University, Wah Campus Pakistan in 2015, where he is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering at Huazhong University of Science and Technology (HUST), Wuhan China. His research interests include power system optimization, and planning optimizarion of renewable energy resources and distributed generation.

show abstract

“…The ISE model that can truly represent the characteristics of distribution systems is of great significance to DSSE [ 13 , 14 ]. Note that most of models utilized in DSSE are similar to those of transmission system and the bus voltage or branch current are taken as the state variables [ 15 , 16 ]. In addition, the branch injection power is taken as the state variables so as to reduce the influence of bad data on the state estimation results [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…Note that most of models utilized in DSSE are similar to those of transmission system and the bus voltage or branch current are taken as the state variables [ 15 , 16 ]. In addition, the branch injection power is taken as the state variables so as to reduce the influence of bad data on the state estimation results [ 16 ]. One possible disadvantage of these models is that the line parameters and power sources are assumed to be consistent.…”

Section: Introductionmentioning

confidence: 99%

Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties

Chen

Ren

Amaratunga

2021

Sensors

View full text Add to dashboard Cite

Distribution system state estimation (DSSE) plays a significant role for the system operation management and control. Due to the multiple uncertainties caused by the non-Gaussian measurement noise, inaccurate line parameters, stochastic power outputs of distributed generations (DG), and plug-in electric vehicles (EV) in distribution systems, the existing interval state estimation (ISE) approaches for DSSE provide fairly conservative estimation results. In this paper, a new ISE model is proposed for distribution systems where the multiple uncertainties mentioned above are well considered and accurately established. Moreover, a modified Krawczyk-operator (MKO) in conjunction with interval constraint-propagation (ICP) algorithm is proposed to solve the ISE problem and efficiently provides better estimation results with less conservativeness. Simulation results carried out on the IEEE 33-bus, 69-bus, and 123-bus distribution systems show that the our proposed algorithm can provide tighter upper and lower bounds of state estimation results than the existing approaches such as the ICP, Krawczyk-Moore ICP(KM-ICP), Hansen, and MKO.

show abstract

Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting

Cited by 30 publications

References 35 publications

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

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

A Novel PSOS-CGSA Method for State Estimation in Unbalanced DG-Integrated Distribution Systems

Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties

Contact Info

Product

Resources

About