Fuzzy Logic-Based AVC Relay for Voltage Control of Distribution Network with and without Distributed/Embedded Generation

Salman, S.K.; Wan, Z.G.

doi:10.1109/pct.2007.4538647

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…The conventional control methods are not efficient for mitigating voltage rise, which is caused by DG in the distribution network [28]. The alternative control method is an intelligent control such as artificial neural network [28], fuzzy logic (FL) [29], and state estimation [30].…”

Section: Voltage‐violation Mitigation Strategiesmentioning

confidence: 99%

Review on voltage‐violation mitigation techniques of distribution networks with distributed rooftop PV systems

Nour

Hatata

Helal

et al. 2019

IET Generation, Transmission & Distribution

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Section: Voltage‐violation Mitigation Strategiesmentioning

confidence: 99%

Review on voltage‐violation mitigation techniques of distribution networks with distributed rooftop PV systems

Nour

Hatata

Helal

et al. 2019

IET Generation, Transmission & Distribution

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“…Voltage management in electricity distribution networks has been widely studied in the literature. In [1], the authors use a fuzzy logic algorithm to control the tap position of the transformers. Although this solution may be effective for traditional distribution networks, its implementation in DER-dominated networks is impractical due to the unpredictable and fluctuating nature of the DERs' output.…”

Section: Introductionmentioning

confidence: 99%

Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks

Vassallo,

Benzerga,

Bahmanyar

et al. 2023

2023 International Conference on Clean Electrical Power (ICCEP)

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The increasing adoption of distributed energy resources, particularly photovoltaic (PV) panels, has presented new and complex challenges for power network control. With the significant energy production from PV panels, voltage issues in the network have become a problem. Currently, PV smart inverters (SIs) are used to mitigate the voltage problems by controlling their active power generation and reactive power injection or absorption. However, reducing the active power output of PV panels can be perceived as unfair to some customers, discouraging future installations. To solve this issue, in this paper, a reinforcement learning technique is proposed to address voltage issues in a distribution network, while considering fairness in active power curtailment among customers. The feasibility of the proposed approach is explored through experiments, demonstrating its ability to effectively control voltage in a fair and efficient manner.

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“…In 2015, S.K. Salman et al presented a design of fuzzy logic-based AVC relay for voltage control of distribution network [5]. Fuzzy logic systems are used in the design due to its simple and fast operation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Transformer Tap Changing System

Sarimuthu

Ramachandaramurthy

Mokhlis

et al. 2016

IJAAS

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<p>Voltage control is considered one of the basic operational requirements of electrical power systems. The most popular voltage control equipment includes On-Load Tap Changer (OLTC) transformer controlled by Automatic Voltage Control (AVC) relay. Recent studies have shown that fuzzy inference systems (FIS) are applicable for transformer tap changing system. In this paper, FIS are developed for transformer tap changing system using Mamdani-type and Sugeno-type fuzzy models. The results of the two fuzzy inference systems (FIS) are compared. The basic difference between the Mamdani-type FIS and Sugeno-type FIS is included in this paper. It also shows which one is a more suitable choice of the two FIS for transformer tap changing system.</p>

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Fuzzy Logic-Based AVC Relay for Voltage Control of Distribution Network with and without Distributed/Embedded Generation

Cited by 8 publications

References 4 publications

Review on voltage‐violation mitigation techniques of distribution networks with distributed rooftop PV systems

Review on voltage‐violation mitigation techniques of distribution networks with distributed rooftop PV systems

Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks

Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Transformer Tap Changing System

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