An Improved Droop Control Method for Voltage-Source Inverter Parallel Systems Considering Line Impedance Differences

Ma, Junjie; Wang, Xudong; Liu, Jinfeng; Gao, Hanying

doi:10.3390/en12061158

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…The parallelization of single-phase inverters without communications between them has usually been performed using the droop method, which drives the sinusoidal references of the inverters for sharing the common loads [1][2][3]. The method introduces proportional droops in the inverter frequency ω* and voltage amplitude V* references, respectively, according to the P and Q load consumed powers.…”

Section: Introductionmentioning

confidence: 99%

A Power Calculation Algorithm for Single-Phase Droop-Operated-Inverters Considering Linear and Nonlinear Loads HIL-Assessed

et al. 2019

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The active and reactive powers, P and Q, are crucial variables in the parallel operation of single-phase inverters using the droop method, introducing proportional droops in the inverter output frequency and voltage amplitude references. P and Q, or P-Q, are calculated as the product of the inverter output voltage and its orthogonal version with the output current, respectively. However, when sharing nonlinear loads these powers, Pav and Qav, should be averaged by low-pass filters (LPFs) with a very low cut-off frequency to avoid the high distortion induced by these loads. This forces the droop method to operate at a very low dynamic velocity and degrades the system stability. Then, different solutions have been proposed in literature to increase the system velocity, but only considering linear loads. Therefore, this work presents a method to calculate Pav and Qav using second-order generalized integrators (SOGI) to face this problem with nonlinear loads. A double SOGI (DSOGI) approach is applied to filter the nonlinear load current and provide its fundamental component to the inverter, leading to a faster dynamic velocity of the droop-based load sharing capability and improving the stability. The proposed method is shown to be faster than others in the literature when considering nonlinear loads, while smoothly driving the system with low distortion levels. Simulations, hardware-in-loop (HIL) and experimental results are provided to validate this proposal.

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Section: Introductionmentioning

confidence: 99%

A Power Calculation Algorithm for Single-Phase Droop-Operated-Inverters Considering Linear and Nonlinear Loads HIL-Assessed

et al. 2019

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“…Similarly, the control formula for Q-V is given by: Here, the reactive power (Q) is adjusted using a control coefficient 'n' and a reference value (Q*), which determines the sensitivity to voltage fluctuations. E represents the current system voltage, while E* indicates the desired voltage, typically aligned with the nominal or expected voltage [30,31]. Figure 1 depicts the P/Q droop characteristic for the q-axis and d-axis, following Equations ( 1) and ( 2).…”

Section: Foundations and Practical Implementations Of Pcmentioning

confidence: 99%

Enhancing microgrid performance with AI‐based predictive control: Establishing an intelligent distributed control system

Hasani,

Heydari,

Golsorkhi

2024

IET Generation Trans & Dist

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Microgrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real‐time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN‐based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability.

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“…The droop-based local control VSIs, which are parallelized for sharing loads, need to calculate P-Q to generate the sinusoidal references to follow, as can be seen in References [17,[26][27][28][29][30]. Moreover, considering a mainly inductive line transmission is desirable for the P-Q calculations and has yielded beneficial results in low-voltage systems [18,[30][31][32][33].…”

Section: Droop-based Local Control Techniques Against Nonlinear Loadsmentioning

confidence: 99%

HIL-Assessed Fast and Accurate Single-Phase Power Calculation Algorithm for Voltage Source Inverters Supplying to High Total Demand Distortion Nonlinear Loads

et al. 2020

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The dynamic performance of the local control of single-phase voltage source inverters (VSIs) can be degraded when supplying to nonlinear loads (NLLs) in microgrids. When this control is based on the droop principles, a proper calculation of the active and reactive averaged powers (P–Q) is essential for a proficient dynamic response against abrupt NLL changes. In this work, a VSI supplying to an NLL was studied, focusing the attention on the P–Q calculation stage. This stage first generated the direct and in-quadrature signals from the measured load current through a second-order generalized integrator (SOGI). Then, the instantaneous power quantities were obtained by multiplying each filtered current by the output voltage, and filtered later by utilizing a SOGI to acquire the averaged P–Q parameters. The proposed algorithm was compared with previous proposals, while keeping the active power steady-state ripple constant, which resulted in a faster calculation of the averaged active power. In this case, the steady-state averaged reactive power presented less ripple than the best proposal to which it was compared. When reducing the velocity of the proposed algorithm for the active power, it also showed a reduction in its steady-state ripple. Simulations, hardware-in-the-loop, and experimental tests were carried out to verify the effectiveness of the proposal.

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An Improved Droop Control Method for Voltage-Source Inverter Parallel Systems Considering Line Impedance Differences

Cited by 11 publications

References 33 publications

A Power Calculation Algorithm for Single-Phase Droop-Operated-Inverters Considering Linear and Nonlinear Loads HIL-Assessed

A Power Calculation Algorithm for Single-Phase Droop-Operated-Inverters Considering Linear and Nonlinear Loads HIL-Assessed

Enhancing microgrid performance with AI‐based predictive control: Establishing an intelligent distributed control system

HIL-Assessed Fast and Accurate Single-Phase Power Calculation Algorithm for Voltage Source Inverters Supplying to High Total Demand Distortion Nonlinear Loads

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