Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
With the penetration of distributed generation (DG) units, the power systems will face insecurity problems and voltage stability\nissues.This paper proposes an innovatory method by modifying the conventional continuation power flow (CCPF) method. The\nproposed method is realized on two prediction and correction steps to find successive load flow solutions according to a specific\nload scenario. Firstly, the tangent predictor is proposed to estimate the next predicted solution from two previous corrected\nsolutions. And then, the corrector step is proposed to determine the next corrected solution on the exact solution.This corrected\nsolution is constrained to lie in the hyperplane running through the predicted solution orthogonal to the line from the two\nprevious corrected solutions. Besides, once the convergence criterion is reached, the procedure for cutting the step length control\ndown to a smaller one is proposed to be implemented. The effectiveness of the proposed method is verified via numerical\nsimulations on three standard test systems, namely, IEEE 14-bus, 57-bus, and 118-bus, and compared to the CCPF method....
Distribution line is one of the most important components of the distribution system. Troubleshooting faults on these lines are\noften a tedious task requiring service vehicles and personnel moving from one place to another in order to locate the fault and fix\nthe problem. The study, therefore, is on how a composite fault location technique can be applied to predict the location of faults on\nthe distribution lines. The calculations for the estimation of the fault location are performed using one terminal voltage and\ncurrent data of the distribution line. A composite method that combines the impedance-based method and the fuzzy inference\nsystemmethod is used in the fault location algorithm. The presented algorithm has been extensively tested using the MATLABSimulink\nmodel of a 33KV 40-kilometer distribution line. The simulation result demonstrates good accuracy and robustness of\nthe algorithm....
This paper delineates a conventional buck converter controlled by optimized\nPID controller where Genetic Algorithm (GA) is employed with a view to\nenhancing the performance by analyzing the performance parameters. Genetic\nAlgorithm is a probabilistic search algorithm which is substantially used\nas an optimization technique in power electronics. A bunch of modifications\nhave already been introduced to enhance the performance depending upon\nthe applications. However, in this paper, modified genetic algorithm has been\nused in order to tune the key parameters in the converter. Hence, an analysis\nis carried out where the performance of the converter is illustrated in terms of\nrise time, settling time and percentage of overshoot by deploying GA based\nPID controller and the overall comparative study is presented. Responses of\nthe overall system are accumulated through rigorous simulation in MATLAB\nenvironment....
Nowadays, power electronic technology is widely affecting peopleâ??s daily work and life. However, there are still many problems in\nthe current power supply research. When the fault information of power transformer is not complete or there is some ambiguity\nor even the information is lost, it will largely lead to the conclusion and correct conclusion of fault diagnosis. In this case, the fuzzy\ntheory is applied to the fault diagnosis of shunt capacitor, and the fuzzy fault diagnosis systemof shunt capacitor is studied. At the\nsame time, a map-based fault diagnosis system is proposed. In this paper, the cloud computing technology is introduced into the\ndeep learning and compared with SVM and DBN algorithm. The research results of this paper show that the accuracy of fuzzy\ndiagnosis results is 94%, 84%, 90%, 80%, 83%, and 70%, respectively, which shows that the model diagnosis reliability is relatively\nhigh. Among the three algorithms, MR-DBN overall detection rate is higher and the time-consuming is lower than the other two\nmethods. The diagnostic accuracy and misjudgment rate of DBN are as follows: 96.33% and 3.90%. The diagnosis accuracy and\nmisjudgment rate of SVM are as follows: 96.40% and 3.83%. The diagnostic accuracy and misjudgment rate of MR-DBN are,\nrespectively, 99.52% and 0.57%. Compared with the other two methods, MR-DBN has the highest diagnostic accuracy and the\nlowest error rate, which to a large extent indicates that MR-DBN algorithm has higher diagnostic accuracy and has greater\nadvantages and reliability in power supply diagnosis and identification. It not only improves the accuracy of power capacitor fault\ndiagnosis and identification but also provides a new method for the application of power capacitor fault research\nand development....
In this research, a differential protection technique for a power transformer is proposed by using random forest and boosting\nlearning machines. The proposed learning machines aim to provide a protection expert system that distinguishes between\ndifferent transformer status which are normal, inrush, overexcitation, CT saturation, or internal fault. Data for 20 different\ntransformers with 5 operating cases are used in this research. The utilized randomforest and boosting techniques are trained using\nthese data. Meanwhile, the proposed models are validated by other measures such as out-of-bag error and confusion matrix. In\naddition, variable importance analysis that shows signalâ??s component importance inside a transformer at different instances is\nprovided. According to the result, the proposed randomforestmodel successfully identifies all of the current cases (100% accuracy\nfor the conducted experiment). Meanwhile, it is found that it is less accurate as a conditionalmonitoring element with accuracy in\nthe range of 97%â??98%. On the other hand, the proposed boostingmodel identifies all of the currents for both cases (100% accuracy\nfor the conducted experiment). In addition to that, a comparison is conducted between the proposed models and other AI-based\nmodels. Based on this comparison, the proposed boosting model is the simplest and the most accurate model as compared to\nother models....
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