Intelligent coordinators for automatic voltage regulator and power system stabiliser in a multi‐machine power system

Khezri, Rahmat; Oshnoei, Arman; Yazdani, Amirmehdi; Mahmoudi, Amin

doi:10.1049/iet-gtd.2020.0504

Cited by 25 publications

(16 citation statements)

References 36 publications

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“…PSS is responsible to compensate for the phase lag between the exciter input and electrical torque. The block diagram associated with the dynamic models of PSS and AVR can be found in [13]. The PSS-PSS1A model used in this work is described as [20].…”

Section: Mthematical Model Of Pss and Avrmentioning

confidence: 99%

“…Throughout the action exploration procedure, a random noise 𝜗 is added to develop the exploration policy 𝜇 ′ as 𝜇 ′ (𝑠 𝑡 ) = 𝜇(𝑠 𝑡 |𝜃 𝑡 𝜇 )+𝜗 𝑡 (13) where 𝜗 𝑡+1 = 𝜗 𝑡 × 𝑟 𝑐 . In this paper, the actor-network is employed to assess the property of the actions.…”

Section: Drl Based Intelligent Coordinatormentioning

confidence: 99%

“…Fixed parameters of the PSS and AVR may degrade the grid stability in faulty conditions. Artificial intelligence methods such as neural network, brain emotional learning, and fuzzy logic are recognized as potential options for coordination development among PSS and AVR in power grids [12][13]. The important feature of these techniques is the independent-model structures that allow techniques to control the power grid's uncertainty, intricacy, and nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Coordinated Control of AVR and PSS in Power Systems: A Deep Reinforcement Learning Method

Oshnoei

Sadeghian

Mohammadi‐Ivatloo

et al. 2021

2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Syst

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In this paper, a strategy based on deep reinforcement learning (DRL) as an intelligent coordinator for power system stabilizer (PSS) and automatic voltage regulator (AVR) in a two-are power grid is proposed. The proposed coordinator is developed to provide accurate online modification of the gains appearing in the structure of PSS and AVR which avoids unfavorable interactions between PSS and AVR under significant changes in the working point and thereby guaranteeing the stability of the power grid. A Markov decision manner is used to formulate the DRL problem and it is solved through a deep deterministic policy gradient approach with an actor-critic framework. Since the intelligent coordinator relies on the expert's science, some scaling coefficients are added to the coordinator body to achieve optimal performance. To confirm the effectiveness of the presented DRL approach, the design is conducted on Kundur's power grid. Simulations illustrate that the proposed DRL-based control can confirm the stability of the system and attain desired dynamic responses.

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Section: Mthematical Model Of Pss and Avrmentioning

confidence: 99%

Section: Drl Based Intelligent Coordinatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Coordinated Control of AVR and PSS in Power Systems: A Deep Reinforcement Learning Method

Oshnoei

Sadeghian

Mohammadi‐Ivatloo

et al. 2021

2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Syst

Self Cite

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“…Driven by the need for maintaining the terminal voltage stability in power systems, different adaptive control approaches [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], such as neural network methods [11] and optimization approaches [12] of AVRs, are being explored for terminal voltage stability purposes. An efficient, robust, and adaptive AVR control is necessary due to uncertainty, nonlinearity, and high-level penetration of distributed energy resources in modern power grids.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…However, they did not assess the AVR performance in terms of wide-area voltage stability and dynamic response to contingencies, e.g., N-1 or severe fault analysis. In [19], three different intelligent methods, i.e., fuzzy logic, artificial neural network, and brain emotional learning coordinators are used for coordinating AVR and PSS in a multimachine power system. e proposed algorithm is only evaluated on a small, four-machine test system, not in a wide-area and complex power system.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems

Abegaz

Zarrabian

2022

International Transactions on Electrical Energy Systems

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The state-estimation and optimal control of multigeneration systems are challenging for wide-area systems having numerous distributed automatic voltage regulators (AVR). This paper proposes a modified Q-learning method and algorithm that aim to improve the convergence of the approach and enhance the dynamic response and stability of the terminal voltage of multiple generators in the experimental Western System Coordinating Council (WSCC) and large-scale IEEE 39-bus test systems. The large-scale experimental testbed consists of a six-area, 39-bus system having ten generators that are connected to ten AVRs. The implementation shows promising results in providing stable terminal voltage profiles and other system parameters across a wide range of AVR systems under different test scenarios including N-1 contingency and fault conditions. The approach could provide significant stability improvement for wide-area systems as compared to the implementation of conventional methods such as using standalone AVR and/or power system stabilizers (PSS) for the wide-area control of power systems.

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Intelligent Approaches to Support Demand Response in Microgrid Planning

Khezri

Mahmoudi

Aki

2022

Intelligent Data Mining and Analysis in Power and Energy Systems

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Intelligent coordinators for automatic voltage regulator and power system stabiliser in a multi‐machine power system

Cited by 25 publications

References 36 publications

Data-Driven Coordinated Control of AVR and PSS in Power Systems: A Deep Reinforcement Learning Method

Data-Driven Coordinated Control of AVR and PSS in Power Systems: A Deep Reinforcement Learning Method

Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems

Intelligent Approaches to Support Demand Response in Microgrid Planning

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