State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks

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.

Distribution System State Estimation Using Model-Optimized Neural Networks

“…This paper takes off from a previous work shown in [36], where the standard MSE loss function has been replaced with the WLS loss function, which is used traditionally by power system state estimation to help the neural network model learn which of the measurements to trust more. By coming off of Equation ( 2), the weight, W, is used with predetermined weights that correspond to the measurement devices known to the operator.…”

“…The paradigm this paper takes is inferring the system state given the known system states some time steps before as shown in Figure 3, i.e., approximating h(x) and learning to consider or reject . Furthermore, reference [36] used a special kind of neural network structure to improve the accuracy of estimation. This is the long short-term memory (LSTM) neural network, which is an improved variation of the recurrent neural networks (RNNs).…”

“…The test system is a modified IEEE 34-bus test system from [38] containing a 9-bus DC microgrid model developed by [36] connected together via a bidirectional interlinking converter as shown in Figure 8. The IEEE 34-bus test system is a grid-connected distribution system composed of all kinds of loads by phase connection and by bus distribution with a maximum rating of about 150 kW per phase.…”

Integrated Control and Protection Architecture for Islanded PV-Battery DC Microgrids: Design, Analysis and Experimental Verification

“…and energy storage systems (e.g., battery bank, flywheel, supercapacitor) which deliver power to local loads [1,2]. In the microgrid concept, the energy storage device plays a key role in the demand-supply balance, which helps during islanding and re-synchronizing between the utility grid and microgrid [3]. Thus, it can tackle the energy crisis, and improve grid efficiency.…”

“…Dc microgrids possess plenty of merits compared to classical ac ones. Lower converter requirements, a higher transmission capacity, higher power quality, power transmission loss reductions and a negligible skin effect represent the merits of the dc microgrids over ac networks [3][4][5][6][7][8][9]. Despite their remarkable merits, dc microgrid protection poses many challenges [7][8][9][10][11][12].…”