Voltage Sag State Estimation using Compressive Sensing in Power Systems

Blanco-Solano, Jairo; Suárez, Johann Farith Petit; Ordóñez-Plata, Gabriel; Kagan, Nelson

doi:10.1109/ptc.2019.8810771

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…b. When applying the update strategies of the velocity and position in (32) and (33), due to the random variable ρ in (33), it is difficult to get a swarm, in which all particles can satisfy the constraint conditions during each iteration, resulting in an endless loop. Therefore, for each particle, when the position vector X i updated by using (33) does not meet the constraint conditions, X i is corrected in such a way that if [X i ]> n, n bits which are randomly selected from the current bits equal to 1 of X i are set to 1, whereas if [X i ]< n, (n− [X i ]) bits which are randomly selected from the current bits equal to 0 of X i are set to 1.…”

Section: B Optimization Methods Based On Bpsomentioning

confidence: 99%

“…In this numerical study, according to [30], [31], the transition resistance follows a normal distribution with mean µ = 5 and a standard deviation σ = 1 . The occurrence probability of 3PF, SLGF, LLF and DLGF are assigned as ω 1 = 0.04, ω 2 = 0.73, ω 3 = 0.06 and ω 4 = 0.17, respectively [32]. Besides, when faults are simulated, each bus is considered as fault position and five fault positions with equal interval on each line are also considered, which are randomly selected from a uniform distribution.…”

Section: Numerical Studymentioning

confidence: 99%

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A BPSO-Based Method for Optimal Voltage Sag Monitor Placement Considering Uncertainties of Transition Resistance

Jiang

Liu

et al. 2020

IEEE Access

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This study presents a new approach to the optimal placement of voltage sag monitors considering the uncertainties associated with transition resistance. The influence of transition resistance on the magnitude of voltage sags triggered by symmetrical and unsymmetrical faults is analyzed. Then the transition resistance interval set array for voltage sags is established, on the basis of which, a random vector model on voltage sag observability is proposed and related observability indices are defined in the form of conditional probability. The optimal placement model is established by taking the available number of monitors as the constraint condition and the maximum sag global observability index as the objective function. Binary particle swarm optimization (BPSO) is implemented to obtain the optimal placement results. Finally, simulation is carried out on IEEE 30-bus system, and it is shown that the proposed optimal monitor placement method is more applicable compared with the traditional MRA method. INDEX TERMS Binary particle swarm optimization, conditional probability, observability indices, optimal monitor placement, random vector model, transition resistance, uncertainties, voltage sags. I. INTRODUCTION Voltage sags are the most frequently occurring power quality disturbances, mainly caused by faults in a power system. Voltage sag is typically defined as the reduction of RMS voltage from 0.1 to 0.9 p.u. with a typical duration of 0.5 cycle to 1 min, which is usually characterized by its magnitude (the magnitude of during-fault voltages) and duration (the time during which the RMS voltage stays below a given threshold, usually 0.9 p.u.) [1]-[4]. Many studies conducted around the world have shown that voltage sags cause customers of various sectors significant financial losses, for instance, in a semiconductor manufacturing industry, economic losses per voltage sag have been estimated 3.8 million e[5], [6]. But it is unrealistic to expect that the grid will provide a completely financialloss-free power quality environment for all customers [6]. Before implementing adequate countermeasures, it is necessary to establish a monitoring system by using appropriate power quality monitors (PQMs) and this system should The associate editor coordinating the review of this manuscript and approving it for publication was Hui Ma .

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