Power management in hybrid ANFIS PID based AC–DC microgrids with EHO based cost optimized droop control strategy

Prasad, T. Narasimha; Devakirubakaran, S.; Muthubalaji, S.; Srinivasan, S.; Karthikeyan, B.; Palanisamy, R.; Bajaj, Mohit; Zawbaa, Hossam M.; Kamel, Salah

doi:10.1016/j.egyr.2022.11.014

Cited by 25 publications

(9 citation statements)

References 28 publications

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“…Two case studies on a modified 14-bus medium voltage CIGRE benchmark microgrid are used to validate the algorithm. An autonomous neuro-fuzzy assisted droop control power management for hybrid AC/DC microgrids is presented in [101]. The AC and DC microgrids are controlled by an adaptive neurofuzzy inference system and PID controller with the inte- gration of photovoltaic, wind, and battery.…”

Section: DC Microgrid Power Managementmentioning

confidence: 99%

A Critical Review on DC Microgrids Voltage Control and Power Management

Al-Ismail

2024

IEEE Access

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Direct current (DC) microgrids are becoming increasingly important due to a number of causes, including the widespread use of DC loads, the integration of solar photovoltaic (PV) and energy storage devices (ESDs), and the absence of frequency and reactive power control issues. The control of DC bus voltage, power management, effective power split among the ESDs, and state of charge (SoC) restorations are important in a DC microgrid. However, DC bus voltage control and power management are difficult since the microgrids connect several distributed generators (DGs), loads, utility grids, and ESDs to the DC bus using power electronic converters. It is imperative to properly control the DC bus voltage and manage power among the sources and loads in order to maintain the stability and reliability of DC microgrids. DC microgrids can be controlled by employing centralized, decentralized, distributed, multi-level, and hierarchical control systems to ensure safe and secure operation. Besides, advanced control techniques, such as nonlinear, robust, model predictive, artificial intelligence, and many others, are employed. This article critically reviews two main aspects of DC microgrids: voltage control and power management. The challenges and opportunities for voltage control and power management in DC

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Section: DC Microgrid Power Managementmentioning

confidence: 99%

A Critical Review on DC Microgrids Voltage Control and Power Management

Al-Ismail

2024

IEEE Access

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“…It was further stated that there was a 21% settling time for the ANFIS controller as compared with other conventional controllers. Research conducted on a droop control strategy of power management in hybrid AC-DC microgrids based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller and proportional integral derivative (PID) controller in [17] revealed that the ANFIS controller outperformed that of the PID controller in management of power. However, in the same work [17], an optimizer based on the elephant herding optimization algorithm (EHOA) was formulated to reduce the running cost prices for PV, wind, and battery power.…”

Section: Introductionmentioning

confidence: 99%

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Odonkor

2024

IJEETC

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Power supply management is a critical problem in the operation of a distribution network, considering the causes of large power losses and interruptions in the main grid caused by the unknown connection of loads and DGs that affect power delivery to customers downstream of a distribution network. The above-mentioned problems can be reduced by the integration of microgrids close to load centers and developing a controller using adaptive control techniques to enhance reliable power supply. For this reason, this paper presents an intelligent method for distributed generators' energy control and power dispatch of microgrids integrated into a distribution network employing an Adaptive Neuro- Fuzzy Inference System (ANFIS). The aim is to control distributed generators energy sources, loads, and power dispatch of grid-connected microgrids among multiconnected power sources to maintain a stable power supply without using any optimization techniques. The proposed intelligent ANFIS system is trained for power-sharing purposes and applied to the microgrid controllers. The mathematical modeling of distributed generators, system design, simulation, and testing of the proposed method were done using MATLAB/Simulink software. The results show that the proposed controller is capable of power dispatch and controls the energy harvest of distributed generators. Additionally, it can assign microgrid power source(s) to additional load(s) connected to the active distribution network without interruptions of power flow. The obtained results outperform similar works that used hybrid ANFISPID (Adaptive Neural Fuzzy Inference System-Proportional- Integral-Derivative), PSO-ANFIS (Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System), and GA-ANFIS (Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System) by automatically connecting and controlling distributed generators energy sources with effective power dispatch in mitigating downtime of grid power operations.

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“…The ANFIS approach is implemented in this paper to enhance the performance of a single-phase PFC rectifier; hence, it ensures a controlled dc-link voltage regardless of the dynamic parameters' changes such as load and reference output voltage. Moreover, the ANFIS controller is used to generate the peak value of the reference current for the inner current loop 32 , 33 .…”

Section: Introductionmentioning

confidence: 99%

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

Babes,

Latrèche,

Bouafassa

et al. 2024

Sci Rep

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This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system’s robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

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Power management in hybrid ANFIS PID based AC–DC microgrids with EHO based cost optimized droop control strategy

Cited by 25 publications

References 28 publications

A Critical Review on DC Microgrids Voltage Control and Power Management

A Critical Review on DC Microgrids Voltage Control and Power Management

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

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