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

Abstract

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 .

Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network

“…Here, it is noted that the number and placement of monitors play an important role for sag source location and sag type recognition. According to the method presented in [33], 4 meters are located at bus 2, bus 15, bus 21, and bus 25, respectively, as shown in Fig. 5.…”

“…A reasonable approach is to install only a limited number of monitors at the selected buses where the whole power system is observable. Here, we take the method proposed in [33] as reference for monitor selection, where uncertainties associated with transition resistance are considered. It has been proven that this method achieves more applicable and satisfactory performance compared with the tra-ditional methods [33].…”

“…To further test the performance of the proposed model, we compare it with the GRU-based model. Here, the monitor placements at buses 2, 15, 21, 25, as illustrated in [33], are adopted for fair comparison. We observe that as follows.…”

Optimized Placement of Voltage Sag Monitors Considering Distributed Generation Dominated Grids and Customer Demands

“…Thus, the optimization conditions are insufficient and it is difficult to find an optimal solution. Subsequent studies helped to determine an optimal placement plan solution by introducing new optimization goals, such as the largest sag observability index (Jiang et al, 2020), largest sag severity index (Ibrahim et al, 2010), largest sag weight coefficient (Šipoš et al, 2021), smallest uncertainty area index (Zhang et al, 2019), and largest immunity index (Luo et al, 2019). These studies can uniquely determine the monitoring plan for sags but mainly focus on the system side and ignore the monitoring demands of sensitive customers.…”

A Voltage Sag Severity Evaluation Method for the System Side Which Considers the Influence of the Voltage Tolerance Curve and Sag Type

“…The sag energy index E vs considers the characteristics of the sag amplitude, duration, and sag waveform, and quantitatively analyzes the voltage sag. Due to the compatibility and universality of E vs , it has attracted widespread attention [10][11][12]. In reference [13], the graphical method is used to determine the voltage energy loss under an asymmetric sag, which can both present the evaluation results intuitively and quickly and evaluate the magnitude of comparison more easily.…”