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The generalized adoption of power-electronic-based loads in residential networks is leading to increasing concern about harmonic distortion levels. At the same time, residential distribution systems are insufficiently monitored to provide a reliable analysis of harmonic limits compliance at the point of common coupling of customers. To address this problem, this paper proposes a practical method to estimate voltage harmonic levels in real time at all connection points of residential consumers. The proposed methodology assumes unknown network parameters and topology according to the limited data availability which is usual in low voltage residential networks. The proposed harmonic estimation method uses information gathered from conventional smart meters typically installed in the network (which register fundamental voltage and power) and from a reduced number of installed power quality monitors. A penetration of power quality meters below 10% has proved to provide accurate estimations of voltage harmonic levels at the whole network. The paper also proposes a methodology to optimally locate these power quality meters in the network. Validation of the methodology is performed in two different European distribution test networks with 55 buses and 471 obtaining satisfactory results. The method’s robustness and the parameters affecting accuracy are also analyzed in the article. [ABSTRACT FROM AUTHOR]

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 fully-connected 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. [ABSTRACT FROM AUTHOR]

Significant financial losses due to voltage sags, short duration interruptions (SDIs), and long duration interruptions (LDIs) have been reported over the past years. The methods proposed in the literature allow us to estimate the annual indices related to process trips, though, as they are confined to voltage sags and LDIs, they do not take into account SDIs. This paper proposes to estimate annual financial losses related to process trip (FLPT) for each customer in the distribution system. The methodology correlates the attempts of reclosers to clear nonpermanent faults along with the concept of process immunity time in order to separately estimate the annual FLPT due to voltage sags, SDIs, and LDIs. This division allows the utility to quantify the severity of each of them and identify geographic areas where mitigation solutions should be deployed to avoid customer complaints and penalties by regulatory agencies. [ABSTRACT FROM PUBLISHER]

Nowadays, modern industrial processes are controlled by equipment highly sensitive to voltage sags and interruptions. Although the proposed methods in the literature allow utilities to estimate annual equipment trips due to voltage sags and permanent interruptions, they do not assess explicitly momentary interruptions. This paper proposes a probabilistic methodology that includes the attempts of reclosers to clear nonpermanent faults during the estimation of annual voltage sags and momentary interruptions at each busbar. Furthermore, voltage-tolerance ranges for each type of equipment are taken into account to quantify annual equipment trips due to voltage sags and momentary interruptions for each customer in the distribution network. The results allow the utility to identify a geographic area where sets of customers face high equipment trips during the year. The proposed methodology can be used by utilities to prioritize corrective measures to improve the quality of service to those customers most affected by annual equipment trips. [ABSTRACT FROM PUBLISHER]

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 optimization-based mitigation strategy is proposed to carry out the strategic placement of different flexible ac transmission systems devices based on the analysis of PQ performance and sensitivity analysis. In this methodology, a 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 a large-scale generic distribution network. The advantages and disadvantages of using the proposed indices as the optimization objective functions are also analyzed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a 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 single-event characteristics. 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 analyzed and discussed through comparison with characteristics of five previously reported single-event indices including four numerical indices and one fuzzy voltage sag index. [ABSTRACT FROM PUBLISHER]

This paper presents a new stochastic approach to the comprehensive assessment of the impact of voltage sags on 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 the 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, called 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 analyzed and discussed in the paper. [ABSTRACT FROM PUBLISHER]

This paper introduces a stochastic methodology to assess annual performance of distribution network and individual customers with respect to voltage sags, long duration interruptions, equipment trips, and process trips. These values are then used to evaluate financial losses in the entire distribution network. For each of the large number of simulated faults throughout the network, a probability of sag and long duration interruption occurrences is evaluated at each network bus. The methodology then includes for the first time the concept of process immunity time in order to assess as accurately as possible the resulting financial loss due to process interruption. To increase the accuracy of the assessment of financial loss, the methodology also considers the possibility of equipment and process restart, reliability, and type of fault. The methodology is illustrated on a real distribution network and it shows that inclusion of PIT in financial loss assessment results in a reduction of about 25% in estimated financial loss. [ABSTRACT FROM AUTHOR]

This paper investigates the factors that influence the outcome of financial-loss analysis in voltage sag studies. Focusing on industrial processes, this paper considers the effects of voltage sag profile estimation, plant-load profile modelling, and process cycle modelling on the financial risk associated with process failure. Based on voltage sag monitoring records, sag profile is probabilistically modeled for the estimation of future sag characteristic. Variations in financial risk due to varying process activities in different stages of a process (process cycle) and load profile are also investigated. The effects of the individual factors are analyzed through Monte Carlo simulation. The stochastic net present value method is introduced as a financial analysis tool. [ABSTRACT FROM AUTHOR]

This paper describes a methodology to estimate post process failure cost of downtime (COD). COD can be estimated using either historical or direct online costs (direct and restart costs) as input to the model. Application of the proposed COD estimation model is illustrated on an actual industrial process that experienced several downtime events due to voltage sags in its electric supply. Raw financial data obtained from industrial collaborators are normalized and used as an input to developed software for COD estimation. Several case studies, including influence of simultaneous manufacturing of different product variants and optimization (minimization) of COD are presented and discussed in detail. [ABSTRACT FROM AUTHOR]

This paper presents an approach to optimally select and allocate flexible ac transmission (FACTS) devices in a distribution network in order to minimize the number of voltage sags at network buses. The method proposed is based on the optimization of a preselected objective function using simple and niching genetic algorithms (GA). The objective of the optimization is to achieve the improvement in overall system sag performance of the network. Using proposed GA-based optimization, the location, the type and the rating of six (in total) FACTS devices are optimized simultaneously. Three types of FACTS devices are implemented in this study, namely, static var compensator, static compensator, and dynamic voltage restorer. The performance of the proposed algorithm is tested and illustrated on 295-bus generic distribution system. [ABSTRACT FROM PUBLISHER]

Flexible ac transmission system (FACTS) devices or their subderivative custom power devices are efficient and often used and recommended power-electronics-based devices for mitigation of voltage sags in electrical power system. With FACTS devices installed, the overall system (and individual bus) sag performance could significantly change depending on the type of the device used. In order to assess this change in sag performance in realistic large power systems, the classical (essentially static) fault calculation procedure should be amended to incorporate the effects of these devices on bus voltages. This paper presents a new approach of quasistatic sag analysis using a system impedance (Z bus ) matrix that incorporates FACTS devices. Three types of FACTS devices which are most often used in practical applications are considered in this study: static compensator, static var compensator, and the dynamic voltage restorer. The case studies based on the 295-bus generic distribution system are used to illustrate the modeling method and the effectiveness of these devices in sag mitigation. [ABSTRACT FROM PUBLISHER]

FACTS based devices are proven to be an efficient mitigation solution for voltage sag prevention. The high cost of FACTS based devices often prohibits their wider deployment within power networks. This paper presents an approach for comprehensively assessing the financial benefits to the network resulting from their use. The annual financial losses of the entire network due to voltage sags are used as a savings target. The three most widely used FACTS based devices for voltage sag mitigation, namely, Static VA Compensator (SVC), Static Compensator (STATCOM) and Dynamic Voltage Restorer (DVR) are then optimally placed using a Niching Genetic Algorithm (NGA). The aim is to reduce overall financial losses in the network due to voltage sags. The cost of the individual devices along with their installation costs and annual maintenance are taken into account in the optimization procedure. Since this methodology is largely based on an economic evaluation of the solution, several conventional economic analysis methods are utilized. Simulations are performed on a 295-bus generic distribution network (GDN). [ABSTRACT FROM AUTHOR]