Development of Intelligent Controlled Microgrid for Power Sharing and Load Shedding

Lin, Faa-Jeng; Tan, Kean Ming; Chang, Chia‐Ming; Li, Mengyang; Tseng, Tzu-Yu

doi:10.1109/tpel.2022.3152167

Cited by 21 publications

(11 citation statements)

References 30 publications

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“…The characteristics of the Q-V and P-ω droop controls adopted in the storage system are represented in Figs. 2(b) and 2(c) and described as follows [4]:…”

Section: A Storage System Using Droop Controlmentioning

confidence: 99%

“…In accordance with the PN theory, the competition law is utilized to select the suitable fired nodes for generating the tokens in the petri layer [4]. The PN is composed of two types of nodes: transition and place.…”

Section: ) Petri Layermentioning

confidence: 99%

“…In the past decade, owing to the penetration of the electric vehicle, charging station, storage system and renewable energy-based distributed generator (DG), the conventional power system has been changed. A novel conception of power system named as microgrid to combine the different DGs with local loads through the power electronic converter has been arisen [1]- [4]. The microgrid can perform in grid-connected mode and islanding mode [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…The droop control strategies are composed of the P-ω and Q-V droop controls. In accordance with the characteristics of the droop controls, the DGs in the islanding microgrid will reduce the voltage amplitude and system frequency to share the reactive power and active power for balancing the supply and demand [4]. Nevertheless, the frequency control and stability of the microgrid need to be considered.…”

Section: Introductionmentioning

confidence: 99%

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Droop Controlled Microgrid With DSTATCOM for Reactive Power Compensation and Power Quality Improvement

Tan

Weng

2022

IEEE Access

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A droop controlled microgrid with distribution static compensator (DSTATCOM) is developed to improve the power quality in this study. Due to the reactive power/voltage Q-V droop characteristic and the existence of the unbalanced, linear inductive and nonlinear loads, the power quality problems, including the voltage drop, unbalanced currents, lagging power factor (PF) and current harmonics, are very serious in the islanded microgrid. Moreover, owing to the instantaneous power following into or out of the DC-link capacitor of the DSTATCOM under load variation, the performance of the DSTATCOM for power quality improvement is seriously degenerated. Hence, to effectively improve the power quality of the droop controlled microgrid and the transient response of the DC-link voltage of the DSTATCOM under load variation, an online trained polynomial petri fuzzy neural network (PPFNN) controller is firstly proposed as the DC-link voltage controller to supersede the conventional proportionalintegral (PI) controller in the DSTATCOM. The network structure and the online learning strategy of the proposed PPFNN are detailedly derived. Finally, the effectiveness of the DSTATCOM using the proposed PPFNN controller to improve the unbalanced currents, the total harmonic distortion (THD) reduction of the current and to compensate the reactive power for the voltage support and PF correction in the droop controlled microgrid is certified.INDEX TERMS Microgrid, droop control, DSTATCOM, power quality, intelligent control, polynomial neural network.

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“…The characteristics of the Q-V and P-ω droop controls adopted in the storage system are represented in Figs. 2(b) and 2(c) and described as follows [4]:…”

Section: A Storage System Using Droop Controlmentioning

confidence: 99%

Section: ) Petri Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Droop Controlled Microgrid With DSTATCOM for Reactive Power Compensation and Power Quality Improvement

Tan

Weng

2022

IEEE Access

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“…By considering the worst points of stability as constraints, an elitist non-dominated sorting genetic algorithm is used to search the better turning points of the droop control curves, reference [20] proposes a multiobjective optimization segmented droop control for DC microgrid. Reference [21] introduces Petri probabilistic wavelet fuzzy neural network (PPWFNN) algorithm to optimize droop control. However, most of the abovementioned optimization compensation algorithms rely on the communication network between the converters, and the computational load is large, which is difficult to implement on hardware devices, and is limited in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Coordinated Nonlinear Gain Droop Control for PV-Battery Hybrid DC Microgrid Access System via a T-S Fuzzy Decision Approach

et al. 2022

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The PV-Battery hybrid DC microgrid is an important structural form for distributed renewable energy microgrid applications. This paper focuses on improving the access utilization rate of PV-Battery energy and enhancing the access stability of the DC bus voltage. Firstly, based on the voltage droop control method for multi-source access system, the relationship between the power margin of PV-Battery energy and the regulation of DC bus voltage deviation is analyzed. Then, a multivariable T-S fuzzy decision approach is used to design a nonlinear virtual resistance-based voltage droop gain coefficient function, which achieves a highly adaptive coordinated distribution of PV-Battery power as well as supports DC bus voltage stabilization. The T-S fuzzy input variables include the DC bus voltage deviation of the access point, the state of charge of the battery, and the PV sunlight intensity. Finally, the simulation system is built employing MATLAB/Simulink, and the feasibility and effectiveness of the proposed strategy are verified through multischeme simulations.INDEX TERMS renewable energy, DC microgrid, T-S fuzzy control, variable gain, coordinated control.

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Advancing microgrid operation: leveraging a load dispatch based-ANN for a multi-source energy integration scenario

2024

Electr Eng

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Development of Intelligent Controlled Microgrid for Power Sharing and Load Shedding

Cited by 21 publications

References 30 publications

Droop Controlled Microgrid With DSTATCOM for Reactive Power Compensation and Power Quality Improvement

Droop Controlled Microgrid With DSTATCOM for Reactive Power Compensation and Power Quality Improvement

Multivariable Coordinated Nonlinear Gain Droop Control for PV-Battery Hybrid DC Microgrid Access System via a T-S Fuzzy Decision Approach

Advancing microgrid operation: leveraging a load dispatch based-ANN for a multi-source energy integration scenario

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