Abstract
The development of state estimators for local electrical energy supply systems is inevitable as the role of the system’s become more important, especially with the recent increased interest in direct current (DC) microgrids. Proper control and monitoring requires a state estimator that can adapt to the new technologies for DC microgrids. This paper mainly deals with the DC microgrid state estimation (SE) using a modified long short-term memory (LSTM) network, which until recently has been applied only in forecasting studies. The modified LSTM network for the proposed state estimator adopted a specifically weighted least square (WLS)-based loss function for training. To demonstrate the performance of the proposed state estimator, a comparison study was done with other SE methods included in this paper. The results showed that the proposed state estimator had high accuracy in estimating the states of DC microgrids. Other than the enhanced accuracy, the deep-learning-based state estimator also provided faster computation speeds than the conventional state estimator.
Highlights
State estimation (SE) is an important component of an energy management system (EMS), since it allows the system operator to control and monitor an electric power system [1]
I=1 where the square of the differences between the actual ith output yactual,i and the predicted ith output ypredicted,i based on the estimated direct current (DC) voltages are multiplied by the corresponding weight of each different measurement type
The distribution system is an example of a section of the power system where real-time measurement is lacking
Summary
State estimation (SE) is an important component of an energy management system (EMS), since it allows the system operator to control and monitor an electric power system [1]. From all the previous works, one can notice that neural networks and deep learning approaches can provide better results over the conventional methods in some applications in terms of accuracy and computational speed. For application of the deep learning method for SE in the microgrid system, the historical data is used in the training process of the state estimator, unlike the conventional WLS SE, which only uses the present time data. Even though both methods used pseudo-measurement, the deep learning method training process disregarded the error variance of all measurement types.
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