No abstract

Abstract--This paper presents a new stochastic approach to comprehensive assessment of the impact of voltage sags in large scale power networks. The approach takes into account the stochastic nature of power system operation including load variation, uncertainty of fault clearing time by protection relays, fault rates of network components and the variation/uncertainty in equipment sensitivity to voltage sags. A new duration zone division method is used to derive sag duration and occurrence frequency based on stochastic distribution of clearing time required by specific protection systems. The adopted probabilistic representation facilitates estimation of network performance based on a single-event characteristic, sag severity index (SSI), developed in the companion paper (Part I). Based on SSI, a new single-site index with respect to voltage sags (Bus Performance Index, BPI S ) is developed to comprehensively represent the overall bus performance with respect to voltage sags. The index is used to identify the areas of the network, named weak areas of the network in this paper, containing buses that are most exposed to potentially disruptive voltage sags. The application, robustness and sensitivity of BPI S are thoroughly analysed and discussed in the paper.Index Terms-Voltage sags, equipment susceptibility, voltage tolerance curves, sag severity index. I. INTRODUCTIONoltage sags cause frequent disruptions to modern industrial processes and malfunction of electronic equipment, resulting in substantial financial losses [1]. This issue, as one of the most critical power quality problems, has become a major focal point for many utilities and industries [2]. To reduce overall financial consequences of voltage sags, it is necessary to assess voltage sag performance of network buses as accurately as possible and consequently to identify the buses that are most affected by voltage sags. Assessing voltage sag performance at certain location requires two steps: calculating single-event characteristic for each sag event; and then calculating single-site indices based on the single-event characteristics of all sag events occurring at the location [3]. Various single-site indices have been proposed in literature to assess voltage sag performance. Sag severity at certain location can be presented through probability density and distribution functions [3]. Furthermore, the sag information can be compressed into a sag table which groups the sags based on the interval of residual voltage and duration [4][5][6][7][8][9], or by using modified reliability based SAIFI index [10][11][12]. These single-site indices simply give the number of events per year within a certain range of magnitude and duration, resulting in discrete representation of sag characteristics. The aforementioned indices are not suitable for comparing the actual voltage sag performance of different buses as they typically do not include multiple sag characteristics at the same time nor, and more importantly, sensitivity of equipment connected at these buses to vo...

This paper is concerned with preview control problems for linear time-varying discrete systems in a multirate setting. First, by using the discrete lifting technique, the multirate time-varying discrete system is converted to a formal single-rate system. Then, by applying the standard linear quadratic (LQ) preview control method, we construct the expanded error system, and the optimal preview control model of the common time-varying discrete system is obtained. The optimal control input of the expanded error system is obtained by using the outcome of optimal control theory on time-varying systems. The controller with preview action is obtained when we transfer our conclusion into the original system. Finally, a numerical example is included to illustrate the validity of the proposed method.

The comparison of chloroplast genome (cpDNA) sequences among different plant species is an important source of plant molecular phylogenetic data. In this paper, the cpDNA sequences of 13 different oil-tea camellia samples were compared to identify an undetermined oil-tea camellia species from Hainan Province. The cpDNA of the samples was sequenced and resequenced, and divergence hotspots and simple sequence repeat (SSR) variations were analyzed. Bayesian inference (BI) and maximum-likelihood (ML) phylogenetic trees were constructed based on the full cpDNA sequences. The cpDNA sequences were 156512∼157089 bp in length and had the circular tetrad structure typical of angiosperms. The inverted repeats (IRs) of different species included varying contractions and expansions. The cpDNA sequences of the samples of the undetermined species of oil-tea camellia from Hainan Province and Camellia gauchowensis from Xuwen County were identical. In total, 136 genes were annotated, including 91 protein-coding genes (PCGs), 37 tRNA genes and 8 rRNA genes. The GC content of the cpDNA was 37.3%. The small single-copy (SSC)/IR boundary was rich in variation. Divergence hotspots were mainly located in the intergenic space (IGS) and coding sequences (CDSs), and there were obvious differences in divergence hotspots among species. The same divergence hotspots were found in Camellia vietnamensis, Camellia gauchowensis and the undetermined species of oil-tea camellia from Hainan Province. A total of 191∼198 SSR loci were detected. Most of the SSRs included A or T, and the distribution of SSRs in the cpDNA was uneven. Different species shared common SSRs and exhibited unique SSRs. Based on the full cpDNA sequences, the evolutionary relationships of different species of Camellia were well identified. The thirteen samples were classified into 2 clades and 6 subclades, and the different sections of Camellia clustered on the same branch in 2 clades and 2 subclades. Camellia vietnamensis was more closely related to the undetermined species of oil-tea camellia from Hainan Province and the sample of Camellia gauchowensis from Xuwen County than to the sample of Camellia gauchowensis from Luchuan County. Camellia osmantha was closely related to Camellia gauchowensis and Camellia vietnamensis. In conclusion, the cpDNA of different oil-tea camellia species has a conserved tetrad structure with certain length polymorphisms. SSRs are expected to be developed as “barcodes” or “identity cards” for species identification. SSR variations and other factors result in abundant divergence hotspots in the CDSs and IGS (one non-CDS region), indicating that full cpDNA sequences can be used for the species identification and phylogenetic analysis of Camellia. Accordingly, the undetermined species of oil-tea camellia from Hainan Province is likely Camellia vietnamensis, Camellia vietnamensis and Camellia gauchowensis may be the same species, and additional genetic evidence is needed to determine whether Camellia osmantha is a new independent species. The previous division of related sections of Camellia may need readjustment based on full cpDNA sequences.

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...