Fuzzy Control for Smart PV-Battery System Management to Stabilize Grid Voltage of 22 kV Distribution System in Thailand

Mansiri, Kongrit; Sukchai, Sukruedee; Sirisamphanwong, Chatchai

doi:10.3390/en11071730

Cited by 11 publications

(10 citation statements)

References 29 publications

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“…When the delay presents a rapid change, the small-signal stability can be expressed as in (10). Which is determined, if a specific delay is considered, ( ) = , applying the Laplace transform to (6) such as (7):…”

Section: B Probabilistic Model Of Time Delaymentioning

confidence: 99%

“…Still, if the operating conditions change over a wide range, the robustness of the control may not be ensured. Therefore, adaptive control methods such as the fuzzy control method [10,11], the combination of linearized models for each point real operation system, in which the design for each model an observer-based state feedback controller a priori to reach a specified performance objective [12]. Another approach that was developed with the Kalman filter method and compare with the method classification and regression tree (CART) is a very popular decision tree (DTs) as an intelligent classifiers method.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Tuning of Power System Stabilizer Using a Damping Control Strategy Considering Stochastic Time Delay

Oscullo

Gallardo

2020

IEEE Access

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The operation of a modern electrical system presents complex operating conditions, and uncertainty factors increased by the presence of variable and flexible loads. This stochastic operating point variation can promote low-frequency oscillations, and like the conventional power system stabilizer (PSS) features static tuning, it does not provide sufficient damping. Today, an electrical system is monitored through online measurements from the wide-area measurement system (WAMS); moreover, it enables stochastic dynamics to be tracked as the remote signals of WAMS system, the time delay due to the communication channel. It is interesting to analyze the incorporation of the PSS of signals from the WAMS without the time delay effect that degrades the small-signal stability (SSS) and, in turn, allows adaptive tuning. Through two stages, at first, the subspace of the operating point is determined based on a distribution of consumption levels. Each operating point considered a random time delay. For each subspace, establish a bank of tuned PSSs. Second, through the optimal classification and the regression decision tree (CART), the classification rules for the subspace are determined, allowing classifying the measurements of WAMS. The proposed methodology is applied to the New England of 66-bus system and Ecuadorian electric system to demonstrate that the adaptive tuning of PSSs significantly improves SSS under different scenarios and is probabilistic robust, considering the time delay.

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Section: B Probabilistic Model Of Time Delaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Tuning of Power System Stabilizer Using a Damping Control Strategy Considering Stochastic Time Delay

Oscullo

Gallardo

2020

IEEE Access

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“…To cope with uncertain dynamics and parameters some fuzzy logic system (FLS) based approaches have been introduced [17]- [19]. For instance, in [20], a FLS based algorithm is developed to guarantee the voltage stability in the PV/battery system and the battery charging and discharging strategy is studied. Similar to [20], in [21], an FLS based controller is presented for the energy storage system such that the stability of the PV system to be ensured.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in [20], a FLS based algorithm is developed to guarantee the voltage stability in the PV/battery system and the battery charging and discharging strategy is studied. Similar to [20], in [21], an FLS based controller is presented for the energy storage system such that the stability of the PV system to be ensured. A fractionalorder fuzzy control approach is developed in [22], for both power and voltage management.…”

Section: Introductionmentioning

confidence: 99%

A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems

et al. 2021

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“…Robust control [8] is an effective way to deal with the uncertainties, while when the operating conditions change over a wide range, the robustness might not be guaranteed. Therefore, adaptive control methods such as the fuzzy control method [9], multiple-model method [10,11], Kalman filter method [12], etc., which can track conditions change a lot, the nonlinearity of power systems is more prominent and the disturbance models more difficult to interpret [13]. Therefore, machine study methods using statistical analysis may be more suitable for this particular application.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Damping Control Strategy of Wind Integrated Power System

Deng¹,

Suo²,

Yang

et al. 2019

Energies

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Random variation of grid-connected wind power can cause stochastic variation of the power system operating point. This paper proposes a new scheme to design an adaptive damping controller by tracking the variation of system operating points and updating the controller’s functions to achieve a robust damping control effect. Firstly, the operating space is classified into different modes according to the classification of wind power outputs. Multiple power system stabilizers (PSSs) are then designed. Secondly, the method of optimal classification and regression decision tree (CART) is utilized for classifying subspaces of system operating point and it is proposed that the on-line measurements from wide area measurement system (WAMS) are used for tracking the dynamic behaviors of stochastic drifting point and thus guide the updating of appropriate PSSs be switched on adaptively. A 16-generator-68-bus power system integrated with wind power is presented as a test system to demonstrate that the adaptive control scheme by use of the CART can damp multi-mode oscillations effectively when the wind power output changes.

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Fuzzy Control for Smart PV-Battery System Management to Stabilize Grid Voltage of 22 kV Distribution System in Thailand

Cited by 11 publications

References 29 publications

Adaptive Tuning of Power System Stabilizer Using a Damping Control Strategy Considering Stochastic Time Delay

Adaptive Tuning of Power System Stabilizer Using a Damping Control Strategy Considering Stochastic Time Delay

A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems

Adaptive Damping Control Strategy of Wind Integrated Power System

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