Multi-agent microgrid energy management based on deep learning forecaster

Mohammadi

IET Science Measure & Tech

et al. 2021

Self Cite

The high applicability of the electrical motor has led to gain attention in condition monitoring to diagnosis the most common type of fault in this machine, bearing element. The emergence of deep neural networks (DNN) provides the opportunity to design a network for early bearing fault diagnosis with high speed and without any additional feature extraction technique. However, robustness against the noise and some deficiencies in fully capturing features are still challenging issues. To resolve this problem, this paper proposes a one module Gabor filter based convolutional neural networks (CNN), namely Gabor convolutional neural network (GCNN), for bearing fault detection and classification. GCNN is a modulated Gabor filter to enhance the ability in capturing temporal features as well as enhance understanding spatial features with fewer parameters and higher robustness against noises and can be considered as a computational efficient deep structure. The simulation results of the bearing fault detection/classification are studied on two different experimental prototypes, including case Western Reserve University (CWRU) and Paderborn University (Paderborn) datasets. The superiority of this method is shown by comparison with accelerated CNN (ACNN), adaptive CNN, standard CNN, support vector machine (SVM), learning vector quantisation (LVQ), and feedforward neural network (FFNN).

Section: Pooling Layersmentioning

confidence: 99%

Modulated Gabor filter based deep convolutional network for electrical motor bearing fault classification and diagnosis

Mohammadi

IET Science Measure & Tech

et al. 2021

Self Cite

“…They are described as follows: Equations (25) and (26) represent the equality constraints necessary to carry out power balancing within the microgrid. In 25 are the admittance and admittance angle between buses i and k; Pgi, Pdi, Qgi and Qdi are the active power and reactive power supply and demand at nodes i; and are the voltage angles at buses i and k. Hence, (27) represents the fact that the active power output of each generator in every node is constrained within their minimum and maximum output. (28) ensures that the reactive power output for all generators stays within their limits at every node and (29) and (30) ensure that the voltage magnitude and voltage angle stay within their limits at every node.…”

Section: A Mathematical Foundation Of Energy Managementmentioning

confidence: 99%

“…A multi-agent-based approach to energy management of microgrids with solar PV, wind turbines, storage systems and conventional generators is described in [27]. The objective function in this case is minimization of energy loss and operational cost of agents.…”

Section: Introductionmentioning

confidence: 99%

Microgrid Energy Management System With Embedded Deep Learning Forecaster and Combined Optimizer

et al. 2020

and Technology. It has three components: a forecasting system, an optimizer and an optimized electrical vehicle charging station as a separate load for the system. The forecasting system is based on a deep learning model utilizing a Long Short-Term Memory (LSTM)-Autoencoder based architecture. The study provides a statistical analysis of its performance over several runs and addresses reliability and running time issues thereby building a case for its adoption. A MIDACO-MATPOWER combined optimization algorithm has been used as the optimization algorithm for energy management which intends to harness the speed of MATPOWER and the search capabilities of Mixed Integer Distributed Ant Colony Optimization (MIDACO) in finding an appropriate global minimum solution. The objective of the system is to minimize the import of power from the main grid resulting in improved self-sufficiency. Finally, an optimized electrical vehicle charging station model to maximize the renewable energy utilization within the facility is incorporated into the same.

“…In [37], aggregated power load in community MG is forecasted via a developed model of a Deep Recurrent Neural Networks (DRNN) with Long Short-Term Memory (LSTM) units. To forecast load demand in MG, applications of deep learning is modeled based on a hybrid of Convolutional Neural Networks (CNN) and a Gated Recurrent Unit (GRU) called CNN-GRU in [38]. In addition, in [38], the proposed hybrid model is compared with other load forecast methods, such as CNN-LSTM, 2-Dimensional (2D) CNN, GRU, LSTM, ARMIA, k-Nearest Neighbor (kNN) and Neural Network Ensemble (NNE).…”

Section: Introductionmentioning

confidence: 99%

“…To forecast load demand in MG, applications of deep learning is modeled based on a hybrid of Convolutional Neural Networks (CNN) and a Gated Recurrent Unit (GRU) called CNN-GRU in [38]. In addition, in [38], the proposed hybrid model is compared with other load forecast methods, such as CNN-LSTM, 2-Dimensional (2D) CNN, GRU, LSTM, ARMIA, k-Nearest Neighbor (kNN) and Neural Network Ensemble (NNE). A Bi-directional LSTM unit-based DRNN model called DRNN Bi-LSTM is proposed in [4] to supply precise aggregated electrical load demand and the forecasting of photovoltaic power production.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

et al. 2020

Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.