Predictive Based Control Algorithm for Hybrid Diesel-Battery Standalone Power Generation System

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

doi:10.1109/icps48983.2019.9067746

Cited by 3 publications

(3 citation statements)

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“…In this step, a vector with the possible values of the inverter current on the next sampling time is computed. First, the vector of the possible inverter output voltage is needed, it is obtained based on the behavior of the inverter switches as expressed in (4). With the help of Kirchhoff's laws, one obtains the mathematical model of the PCC voltage (5).…”

Section: Current Predictive Modelmentioning

confidence: 99%

“…BES is a DC source in nature, it needs a controlled power converter such as a DC-DC buck-boost converter to operate properly and to adapt its output voltage to the DC-link voltage for standalone microgrid application. Moreover, with the help of the controlled power converters, one controls the power flows and protects the storage element [4,5]. However, the integration of non-renewable energy sources (NRESs) and their power converters with the existing diesel generators makes standalone microgrid applications more complex.…”

Section: Introductionmentioning

confidence: 99%

“…These predictions are then evaluated by a cost function to reduce the error between the prediction and its reference. This concept is applied to achieve many tasks in many applications for grid-connected microgrid [11][12][13] and islanding microgrid [4,14], for power management [11,12], power quality [4,13,14], and synchronization [15]. However, predictive control also has drawbacks as reported in [16].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Current Predictive Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%