A Bacterial Foraging Optimization Technique and Predictive Control Approach for Power Management in a Standalone Microgrid

Dubuisson, F.; Chandra, Ambrish; Rezkallah, Miloud; Ibrahim, Hussein

doi:10.1109/epec48502.2020.9320038

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…The dataset used in this study utilizes open datasets TurkishTextCategorizationProject [10] and microarray datasets [11] obtained from the link: https://csse.szu.edu.cn/staff/zhuzx/Datasets.Html. A total of 9 datasets were used with the number of attributes ranging from 24,481 (highest) to 4,026 (lowest).…”

Section: Dataset Usedmentioning

confidence: 99%

“…Zhang et al [9] concentrate on enhancing the performance of BFO by incorporating a multi-colony cooperation strategy, thereby improving the algorithm's optimization capabilities. Dubuisson et al [10] utilize BFO for predictive control in a standalone microgrid, demonstrating its effectiveness in managing power systems. Subhashini et al [11] utilize BFO to fine-tune parameters of an artificial neural network (ANN) through adaptive Harris Hawks weight optimization, resulting in enhanced performance of the ANN model.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data

Badriyah,

Syarif,

Hardiyanti

2024

JOIV : Int. J. Inform. Visualization

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High-dimensional data allows researchers to conduct comprehensive analyses. However, such data often exhibits characteristics like small sample sizes, class imbalance, and high complexity, posing challenges for classification. One approach employed to tackle high-dimensional data is feature selection. This study uses the Bacterial Foraging Optimization (BFO) algorithm for feature selection. A dedicated BFO Java library is developed to extend the capabilities of WEKA for feature selection purposes. Experimental results confirm the successful integration of BFO. The outcomes of BFO's feature selection are then compared against those of other evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO). Comparison of algorithms conducted using the same datasets. The experimental results indicate that BFO effectively reduces features while maintaining consistent accuracy. In 4 out of 9 datasets, BFO outperforms other algorithms, showcasing superior processing time performance in 6 datasets. BFO is a favorable choice for selecting features in high-dimensional datasets, providing consistent accuracy and effective processing. The optimal fraction of features in the Ovarian Cancer dataset signifies that the dataset retains a minimal number of selected attributes. Consequently, the learning process gains speed due to the reduced feature set. Remarkably, accuracy substantially increased, rising from 0.868 before feature selection to 0.886 after feature selection. The classification processing time has also been significantly shortened, completing the task in just 0.3 seconds, marking a remarkable improvement from the previous 56.8 seconds.

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Section: Dataset Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data

Badriyah,

Syarif,

Hardiyanti

2024

JOIV : Int. J. Inform. Visualization

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“…Ji et al (2021) proposed a continuous-control, deep reinforcement learning-based online scheduling method for MGs. Dubuisson et al (2020) proposed a bacterial foraging optimization algorithm for power management in stand-alone MGs. Omaji et al (2020) established a cooperative game model for MGs.…”

Section: Introductionmentioning

confidence: 99%

Optimal Dispatching Strategy of Active Distribution Network for Promoting Local Consumption of Renewable Energy

Xie

Wang

Wang³

et al. 2022

Front. Energy Res.

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Large-scale renewable energy sources (RESs) have been integrated into the active distribution network (ADN). For promoting the local consumption of RESs within ADN, an optimal dispatching strategy was proposed with two-stage hierarchical energy management framework. On the spatial boundary, a two-layer energy management framework was designed with the local optimization layer and the global optimization layer. The local optimization layer was for optimal power flow in the branch feeder with the objective functions of minimizing operation costs and maximizing the consumption of RESs. The global optimization layer was for optimal power flow in the main feeder with the objective functions of minimizing power loss and the voltage deviation of nodes. On the time scale, two-stage optimal dispatching models were established, including the day-ahead optimal models and intra-day optimal models. The day-ahead optimal models identified the operation status of the controllable units, and then the intra-day optimal models were updated with the ultra-short-term forecast results. A risk indicator was introduced to quantify the uncertainty of RES, and a non-dominated sorting genetic algorithm with elite strategy was adopted to solve the multi-objective nonlinear programming problem. An actual project in northern China was used as the testing system. The results of case studies verify that the proposed strategy can effectively realize the maximum local consumption of RESs and support the economic operation of ADN.

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“…Regarding, the small population groups, BPSO and BFO converge faster but BFO shows the smallest computational burden. In [31], the BFO and PSO methods are compared for a real-time calculation of active and reactive power references, both methods show accurate results however, the BFO method possess less calculation steps.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

et al. 2021

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In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

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A Bacterial Foraging Optimization Technique and Predictive Control Approach for Power Management in a Standalone Microgrid

Cited by 8 publications

References 17 publications

Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data

Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data

Optimal Dispatching Strategy of Active Distribution Network for Promoting Local Consumption of Renewable Energy

Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

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