rasool ebrahimi

Tensor completion is one of the ecient methods for restoring datasuch that minimizing the rank of the tensor leads to an appropriate solution.However, it gives a non-convex objective function, which generates an NPhardproblem. To overcome this problem, instead of using the rank function,the trace norm is applied. To solve this problem, Simple Low Rank TensorCompletion (SiLRTC) can be used. In the methods based on trace norm, theSingular Value Decomposition (SVD) is used, which increases computationalcomplexity of these methods with increasing dimensions. In order to reducethe computational complexity of SVD, the approximate SVD can be utilized.In this paper, to accelerate the convergence speed of SiLRTC Algorithm, thenew combined method FTF-SiLRTC is presented. On the other hand, theimages recovered using the mentioned algorithms are generally accompaniedby horizontal and vertical noise lines and have low accuracy. To solve thisdiculty, the total variation (TV) regularization is added to the problem andthe FTF-SiLRTC-TV Algorithm is introduced to solve it with higher accuracy.

Modern distribution system including Distributed Generation (DG) requires reliable and fast islanding detection algorithms in order to determine the grid status. In this paper, a new multi-model classification-based method is proposed, in order to detect islanding condition for photovoltaic units. Decision tree is chosen as the classification algorithm to classify input feature vectors. The final result is based on voting among three decision tree algorithms. First order derivatives of electrical parameters are employed to construct feature vectors. To cover intermittent nature of renewable sources, different generating states for PV unit are assumed. Probable events are simulated under different system operating states to generate classification data set. The proposed method is tested on typical distribution system including the PV unit, different loads, and synchronous generator. This study showed that this method succeeds in highly fast islanding detection. This quick response can be used in micro-grid application as well as anti-islanding strategy. The results revealed that the proposed voting-base algorithm could classify instances with very high accuracy which leads to reliable operation of distributed generation units.

Isolation detection methods are divided into three active, passive and telecommunication categories. In most cases, passive methods are used more than other methods due to low cost, simplicity, high speed and no harmful effect on the power quality of the power system. In these methods, a parameter of the system is considered as an indicator and the system is analyzed by setting threshold values for this parameter. An island system is detected if the desired parameter exceeds the threshold values for it. The major problem with these methods is their large detection area. In this paper, a method for identifying islandization is proposed based on the decision tree algorithm. This method determines islandization by using one or more indicators used in other methods of inactive detection in terms of network complexity. This method has been tested under various possible incidents in grid-grids and various loading systems and distributed generation sources. The results indicate the fast and accurate performance of the proposed method with its inadequate detection area compared with other inactive methods of island identification.