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This study presents an integrated approach for reliability planning and risk estimation in active distribution systems. By incorporating the use of accurate reliability equivalents for different medium voltage/low voltage networks and load subsectors, a probabilistic methodology is proposed to capture both power quality and reliability aspects in power system planning, which potentially avoids the underestimation of system's performance at bulk supply points. A 'time to restore supply' concept, based on security of supply legislation, is introduced to quantify the effect of different network functionalities such as the use of backup supply or automatic/manual reconfiguration schemes. The range of annual reliability indices reported by 14 network operators in the UK is also used for the validation of reliability results, which allows estimating the risk of interruption times above the regulator‐imposed limits. Accordingly, conventional reliability assessment procedures are extended in this study by analysing a meshed urban distribution network through the application of a time‐sequential Monte Carlo simulation. The proposed methodology also acknowledges the use of time‐varying fault probabilities and empirical load profiles for a more realistic estimation of customer interruptions. A decision‐making approach is shown by assessing the impact of several network actions on the accuracy of reliability performance results. [ABSTRACT FROM AUTHOR]

This study introduces a new theoretical interruption model for assessing more accurately the moment in time when interruptions of electricity customers are likely to occur. Recordings of short and long interruptions from two power supply systems are analysed and the similarity between their patterns is identified and then used to introduce a general interruption probability distribution model, defined in stages as multi‐zone theoretical curves. The effectiveness of the proposed theoretical interruption model is firstly verified for a basic test system supplying an aggregate load point whose power profiles (residential, commercial, industrial and mixed load) are engaged in assessing the energy not supplied, and afterwards for a typical UK power supply system consisting of about 15 000 electricity customers. The results show that a correct representation of the moment of interruption performed with the proposed model leads to completely different results than those obtained based on the conventional assumption that the time when interruption occurs is given by a known probability distribution. Moreover, comparisons against reported figures of reliability indices determine the most suitable probability distribution that shall be used to model the initial conditions of the Monte Carlo simulation and accompany the proposed theoretical model throughout the simulation process. [ABSTRACT FROM AUTHOR]

Highlights: [•] Electricity customer interruption process. [•] Up-to-date continuity of supply legislation and requirements. [•] New deterministic algorithm for customer interruption risk assessment. [•] Duration of interruption targets for domestic customers. [•] Reliability performance of power supply systems. [Copyright &y& Elsevier]

The paper proposes an innovative probabilistic clustering concept for aggregate modeling of wind farms (WFs). The proposed technique determines the number of equivalent turbines that can be used to represent large WF during the year in system studies. Support vector clustering (SVC) technique is used to cluster wind turbines (WTs) based on WF layout and incoming wind. These clusters are then arranged into groups, and finally through analysis of wind at the site, equivalent number of WTs for WF representation is determined. The method is demonstrated on a WF consisting of 49 WTs connected to the grid through two transmission lines. Dynamic responses of the aggregate model of the WF are compared against responses of the full WF model for various wind scenarios. [ABSTRACT FROM PUBLISHER]

This paper presents an original and effective application of support vector clustering (SVC) to electrical load pattern classification. The proposed SVC-based approach combines the calculation of the support vectors, carried out by using a classical procedure adopting a Gaussian kernel, with a specifically developed deterministic algorithm to form the clusters. This algorithm exploits the meaningful location of the bounded support vectors (BSVs) to define the outliers, identifying the clusters in function of the distance of the non-BSVs to the BSVs. Its implementation is less computationally intensive than other existing approaches and the cluster formation is driven by a single user-defined threshold. Extended comparison to other clustering methods is included to show the effectiveness of the proposed approach in grouping multidimensional load pattern data into non-overlapping clusters. This effectiveness is confirmed by the calculation of various cluster validity indicators. In particular, the most successful tasks are the identification of the outliers and the more effective formation of small numbers of clusters with respect to other methods. [ABSTRACT FROM AUTHOR]

Abstract: The modelling of wind turbines with doubly-fed induction generator (DFIG) requires consideration of overall aerodynamic, mechanic, electromagnetic and control aspects, even in the case of DFIG representation in steady-state conditions for energy production assessment. This paper firstly summarizes the background concepts for interpreting the characteristic curves of the DFIG. Then, it considers and illustrates the structure and use of a dedicated equivalent circuit based on the incorporation of an apparent resistance in the model. Furthermore, a new method for correcting the experimental data gathered from wind turbines in practical applications is proposed, in order to make these data comparable with the quantities indicated by the manufacturers in the power-wind speed curve of the wind turbines. Application examples are provided by using data of real DFIG machines. [Copyright &y& Elsevier]