Identification of Weak Areas of Power Network Based on Exposure to Voltage Sags—Part I: Development of Sag Severity Index for Single-Event Characterization

Abstract

Abstract--This paper presents new single-event characteristic, sag severity index (SSI) derived with respect to equipment sensitivity to voltage sags. It is more inclusive of associated uncertainties/variation in equipment response to voltage sags than existing sag severity indices. By changing parameter settings the index appropriately accounts for sag duration and adequately addresses the variation in equipment sensitivity. The value of the index changes continuously at the joining regions of different sag severity levels and reflects realistically the sensitivity trend of equipment embedded in voltage tolerance curves. The properties of the SSI are analysed and discussed through comparison with characteristics of five previously reported single-event indices including four numerical indices and one fuzzy voltage sag index.Index Terms-Voltage sags, equipment susceptibility, voltage tolerance curves, sag severity index. I. INTRODUCTIONoltage sags result in substantial financial losses to many utilities and industries, due to the frequent disruptions to industrial processes and malfunction of electronic equipment [1]. This issue is one of the most critical power quality problems, and sag severity assessment has been a focal point for many researchers in the area of Power Quality in the past. As a critical step of mitigation planning, it is necessary to assess the severity of voltage sags accurately.The severity of voltage sags is strongly related to the response of equipment to voltage sags. Therefore, in order to accurately evaluate the impact of voltage sags, the sensitivity of equipment and ultimately industrial processes to voltage sags must be known. The sensitivity curves of specific equipment have been intensively investigated [2][3][4], and a number of international standards have been set up to provide guidelines for system/tool design in terms of ride-through capability to voltage sags, such as the voltage tolerance curves recommended in IEEE 1346 [5]. From the customers' and equipment manufacturers' perspective, step-shaped ITIC curve [6], a successor of CBEMA curve, was proposed and recommended for use after extensive research in computer power supplies. More recently proposed SEMI F47 curve/standard with similar step-shaped curve was proposed to specify the design requirements of minimum voltage sag ridethrough capability for equipment used in semiconductor industry [7]. This standard provides a target for the facility and utility systems, and it has been used to strictly guide the design of semiconductor processing, metrology, and automated test equipment. In IEEE Standard 1564, ITIC and SEMI F47 are recommended as reference curves to calculate voltage sag severity [8]. In the absence of accurate, specific equipment and process sensitivity curves to voltage sags, standards ITIC and SEMI can be, and generally are used to approximate the response of equipment and industrial processes to voltage sags from the perspective of utility side, equipment manufactures and customers in distribution networks [8...

On capability of different FACTS devices to mitigate a range of power quality phenomena

“…In the study, Bus Performance Index (BPI) [24,25] is adopted to evaluate the severity of voltage sag phenomena from the perspective of utilities and customers in distribution networks. This index takes into account various sag characteristics simultaneously as well as sensitivity of equipment to voltage sags.…”

A General Mathematical Model for LVRT Capability Assessment of DER-Penetrated Distribution Networks

“…Authors in [34] and [35] have introduced an index for sag severity to see the equipment tolerance and a distribution network performance under different sag depth and duration. Also, they have represented a frequentist inference for the voltage sag occurrences in the distribution network.…”

Global assessment of power quality performance of networks using the analytic hierarchy process model

“…probability of causing a trip, is calculated based on probabilistic SEMI F47 curves [20], Fig 4. The sag bus performance index BPI S is then calculated based on the severity of each sag event and the expected frequency of the fault causing the event. Further details are presented in [21,22]. 2) Harmonics The harmonic simulations are performed by selecting a number of different types of non-linear loads in addition to the DG units as sources of harmonic current injections.…”