Now showing 1 - 4 of 4
  • Publication
    Probabilistic Under Frequency Load Shedding Considering RoCoF Relays of Distributed Generators
    The activation of Under Frequency Load Shedding (UFLS) is the last automated action against the severe frequency drops in order to re-balance the system. In this paper, the setting parameters of a multistage load shedding plan are obtained and optimized using a discretized model of dynamic system frequency response. The uncertainties of system parameters including inertia time constant, load damping and generation deficiency are taken into account. The proposed UFLS model is formulated as a mixed integer linear programming optimization problem to minimize the expected amount of load shedding. The activation of Rate-of-Change-of-Frequency (RoCoF) relays as the anti-islanding protection of Distributed Generators (DGs) are considered. The MCS method is utilized for modeling the uncertainties of system parameters. The results of probabilistic UFLS are then utilized to design four different UFLS strategies. The proposed dynamic UFLS plans are simulated over the IEEE 39-bus and the large-scale practical Iranian national grid.
      817Scopus© Citations 78
  • Publication
    Fault Detection in Distribution Networks in Presence of Distributed Generations Using a Data Mining Driven Wavelet Transform
    Here, a data mining–driven scheme based on discrete wavelet transform (DWT) is proposed for high impedance fault (HIF) detection in active distribution networks. Correlation between the phase current signal and the related details of the current wavelet transform is presented as a new index for HIF detection. The proposed HIF detection method is implemented in two subsequent stages. In the first stage, the most important features for HIF detection are extracted using support vector machine (SVM) and decision tree (DT). The parameters of SVM are optimised using the genetic algorithm (GA) over the input scenarios. In second stage, SVM is utilised to classify the input data. The efficiency of the utilised SVM-based classifier is compared with a probabilistic neural network (PNN). A comprehensive list of scenarios including load switching, inrush current, solid short-circuit faults, HIF faults in the presence of harmonic loads is generated. The performance of the proposed algorithm is investigated for two active distribution networks including IEEE 13-Bus and IEEE 34-Bus systems.
      537Scopus© Citations 25
  • Publication
    Decision making under uncertainty in energy systems: State of the art
    (Elsevier, 2013-12) ;
    The energy system studies include a wide range of issues from short term (e.g. real-time, hourly, daily and weekly operating decisions) to long term horizons (e.g. planning or policy making). The decision making chain is fed by input parameters which are usually subject to uncertainties. The art of dealing with uncertainties has been developed in various directions and has recently become a focal point of interest. In this paper, a new standard classification of uncertainty modeling techniques for decision making process is proposed. These methods are introduced and compared along with demonstrating their strengths and weaknesses. The promising lines of future researches are explored in the shadow of a comprehensive overview of the past and present applications. The possibility of using the novel concept of Z-numbers is introduced for the first time.
      2264Scopus© Citations 394
  • Publication
    Probabilistic determination of pilot points for zonal voltage control
    (Institute of Engineering and Technology (IET), 2012-01) ; ;
    Owing to the local nature of voltage and reactive power control, the voltage control is managed in a zonal or regional basis. A new comprehensive scheme for optimal selection of pilot points is proposed in this study. The uncertainties of operational and topological disturbances of the power system are included to provide the robustness of the pilot node set. To reduce the huge number of probable states (i.e. combined states of load and topological changes), a scenario reduction technique is used. The resulted optimal control problem is solved using a new immune-based genetic algorithm. The performance of the proposed method is verified over IEEE 118-bus and realistic Iranian 1274-bus national transmission grids.
      501Scopus© Citations 38