Distributed generation unit allocation utilizing expert system with reliability and voltage sag consideration

-Expert System (ES) as a branch of Artificial Intelligence (AI) methodology can potentially help in solving complicated power system problems. This may be more appropriate methodology than conventional optimization techniques when contradiction between objectives appears in reaching the optimum solution. When this contradiction is the hindrance in reaching the required system operation through the application of traditional methods ES can give a hand in such case. In this paper, the knowledge-based ES technique is proposed to reach near-optimum solution which is further directed to the optimum solution through particle swarm optimization (PSO) technique. This idea is known as Hybrid-Expert-System (HES). The proposed idea is used in getting the optimum allocation of a number of distributed generation (DG) units on Distribution System (DS) busbars taking into consideration three issues; reliability, voltage sag, and line losses. Optimality is assessed on the economic basis by calculating money benefits (or losses) resulting from DG addition considering the three aforementioned issues. The effectiveness of the proposed technique is ascertained through example.

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Self-Supervised Voltage Sag Source Identification Method Based on CNN

Mei

Zhang

et al. 2019

Energies

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A self-supervised voltage sag source identification method based on a convolution neural network is proposed in this study. In addition, a self-supervised CNN (Convolutional Neural Networks) voltage sag source identification model is constructed on the basis of the convolution neural network and AutoEncoder. The convolution layer and pool layer in CNN are used to extract the voltage sag characteristics, and the self-supervised network training process is realized based on the principle of AE. In the constructed mode, features which reflect the data characteristics are used rather than artificial features, thus improving the accuracy of practical application. It is unnecessary to input a lot of correct labels before the self-supervised training process. The model can meet the requirements of sag source identification on timeliness, practicability, diversity, and versatility in the context of modern big data. In this study, three-phase asymmetric sag sources in sag sources are classified into more detailed categories according to different fault phases. Therefore, the proposed method can not only identify the voltage sag source, but also accurately determine the specific fault phase. Finally, the optimal parameters of the model are recognized through a case study, and a self-supervised CNN model is established based on the data type of voltage sag. This model extracts features and identifies sag sources through the measured sag data. The superiority of the proposed method is verified by a comparison.

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Distributed generation unit allocation utilizing expert system with reliability and voltage sag consideration

Cited by 3 publications

References 10 publications

Optimum allocation of distributed generation units in distribution power grids with line losses and voltage sag considerations

Optimum allocation of distributed generation units in distribution power grids with line losses and voltage sag considerations

DG Allocation Based on Reliability, Losses and Voltage Sag Considerations: an expert system approach

Self-Supervised Voltage Sag Source Identification Method Based on CNN

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