In this paper, an effective fault location technique is proposed. Using the current samples of the distribution feeder measured at the substation, the proposed technique first determines the type of fault. Furthermore, an artificial neural network (ANN) is trained for each type of fault. The ANNs are trained to estimate the fault distance to the substation (FDANN).The Inputs of the ANNs are data of 3 phase voltages, currents and active powers of the feeder are measured at the substation in pre-fault and fault stages. The proposed method does not need data of loads of consumers. The proposed method is tested on IEEE 34-bus test feeder. Each ANN is trained by operating patterns. In order for ANNs cover the total operating space of the radial distribution network; fault location, fault resistance and loads are changed in each pattern. The outputs of ANNs for the operating test patterns, not presented in the training stage, are shown the accuracy of the ANNs. The trained FDANNs can estimate fault distance to the substation; even the structure of the distribution network is changed. Proposed method is effective while the input data are contained errors of measuring.
This paper presents a new fault location method for an asymmetrical and unbalanced distribution feeder in the presence of distributed generations (DGs). In this study, a modified version of IEEE 34-bus test feeder with two fixed speed wind generators is considered. In the proposed method, at first, the type of short circuit is determined using the 3-phase current data that are measured at the substation. Then, to train and test the Artificial Neural Network (ANN), different operating patterns are produced for each type of short circuit. In order to cover the total operating space of the radial distribution network by ANNs; fault location, fault resistance, load of every node and power of DGs are changed in each pattern. For each type of short circuit, three different ANNs are used for estimation of the distance of fault to the substation and the interconnection of DGs. Inputs of the ANNs are 3-phase voltage and current which measured at the substation and the DG buses in pre fault and post fault stages. The results show low estimation error in testing of ANNs. For exact fault location, the estimated fault distance to the substation is compared with the estimated fault distance to the DG buses. Finally, the sensitivity of ANNs to the measuring error of the input data is examined.
In this paper, a novel approach for generation rescheduling as a preventive control for enhancing dynamic security using neural network is presented.. Critical clearing time (CCT) associated with each fault including the effect of system controllers and limitation, is adopted as dynamic security criteria. A Dynamic Security Analyzer Neural Network (DSANN) is trained to estimate CCTs associated with different system faults. For each given operating point, DSANN evaluate system CCTs by using steady state pre fault operating condition as input pattern. The most interesting feature of the proposed neural network application is evaluation of sensitivity of CCT with respect to generation pattern. These sensitivities are derived from the information stored in the weighting factors of trained DSANN. The sensitivity of CCT is used as a guideline for selecting the most effective pair of generators to reschedule their MW generation in the process of generation rescheduling aimed security enhancement. The proposed method has been demonstrated on the IEEE-39 bus system with promising results for enhancing dynamic security by generation rescheduling using sensitivity characteristic of neural network.
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