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 proposes a voltage sag estimation approach based on a deep convolutional neural network. The proposed approach estimates the sag magnitude at unmonitored buses regardless of the system operating conditions and fault location and characteristics. The concept of system area mapping is also introduced via the use of bus matrix, which maps different patches in input matrix to various areas in the power system network. In this way, relevant features are extracted at various local areas in the power system and used in the analysis for higher level feature extraction, before feeding into a fullyconnected multiple layer neural network for sag classification. The approach has been tested on the IEEE 68-bus test network and it has been demonstrated that the various sag categories can be identified accurately regardless of the operating condition under which the sags occur.

This paper investigates the impact of different FACTS devices on critical power quality (PQ) phenomena including voltage sags, harmonics and unbalance from the perspective of both mitigation effect and potential negative impact. The FACTS devices, including SVC, STATCOM and DVR, are modeled in commercially available software PowerFactory/DIgSILENT to study their impacts on the critical PQ phenomena. Two control strategies, voltage regulation and reactive power compensation, are considered for STATCOM. For DVR, a PI-controller is developed for the purpose of voltage sag mitigation. The merit of the proposed controller is presented by the dynamic response of during-fault voltage and the capability of post-fault voltage recovery. The study is carried out on a large-scale generic distribution network. The impact of various devices on PQ phenomena is assessed using appropriate evaluation methodologies, and the results obtained with and without mitigation are presented and compared using heatmaps.

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Abstract--This paper presents the concept of provision of differentiated quality of electricity supply based on customers' requirements in distribution networks. To fulfill this concept, five new gap indices are proposed to reflect the satisfaction of the received power quality (PQ) performance compared to the thresholds which are set based on customers' requirements regarding the performance of individual PQ phenomenon or the aggregated PQ performance. Using these new indices as objective functions, an optimisation based mitigation strategy is proposed to carry out the strategic placement of different FACTS devices based on the analysis of PQ performance and sensitivity analysis. In this methodology, greedy algorithm is applied to search the optimal mitigation scheme in order to enable the provision of differentiated PQ levels. The feasibility of the proposed mitigation methodology is demonstrated using large scale generic distribution network. The advantages and disadvantages of using the proposed indices as the optimisation objective functions are also analysed in the paper.Index Terms-Quality of supply, power quality, mitigation strategy, FACTS devices. I. INTRODUCTIONower quality (PQ) issues continue to attract significant attention from both utilities and customers. Among PQ phenomena that attract the most attention are voltage sags, voltage unbalance and harmonics. Voltage sags cause frequent disruptions to industrial processes and malfunction of electronic equipment; voltage unbalance issues cause overheating, accelerated thermal ageing of equipment and reduction of efficiency of the load and overall network [1]; and harmonics (voltage distortion) cause thermal stress, insulation stress and load disruption to both power system equipment and customer's equipment [2]. These PQ phenomena result in substantial financial losses to both utilities and industries. Furthermore, the increasing level of penetration of intermittent, power electronics connected renewable resources, electric vehicles and other power electronics interfaced loads results in increasing variability of PQ in power systems. With more sensitive equipment/devices connected to the grids, it is essential to provide acceptable quality of supply as required by customers. To ensure this, a number of international PQ standards, e.g., EN 50160 and IEC 61000 series, have been set up to provide guidelines to utilities regarding the acceptable levels of PQ supply.In reality, requirements on PQ performance vary from area to area (e.g., commercial, residential and industrial areas), depending on the sensitivity of customers' processes and equipment to specific PQ phenomena. Considering different PQ requirements by different parties involved in electricity supply chain, costs associated with PQ mitigation and willingness to contribute to PQ mitigation by different market players, the idea of provision of differentiated levels of quality of supply to different customers in different zones is becoming more and more acceptable. This approach will improve the...