Brain-Inspired Deep Meta-Reinforcement Learning for Active Coordinated Fault-Tolerant Load Frequency Control of Multi-Area Grids

Li, Jiawen; Zhou, Tao; Cui, Haoyang

doi:10.1109/tase.2023.3263005

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…where φα(k) (k) represents the estimate of ϕ α(k) (k), and µ α(k) > 0 is a weighting coefficient. Minimizing the performance index (13), we obtain the following PPD estimation algorithm:…”

Section: Controller Designmentioning

confidence: 99%

“…Recently, the academic community has proposed diverse control strategies for LFC in multi-area power systems, employing various theoretical frameworks. These strategies encompass Model Predictive Control [2,3], Robust Control [4,5], Fuzzy Logic Control [6][7][8], Sliding Mode Control [9,10], Linear Matrix Inequality (LMI) Control [11,12], Reinforcement Learning [13,14], and other methods. Reference [2] integrates dynamic event-triggered mechanisms and a hybrid H2 performance index to design a robust Model Predictive Control (MPC) strategy for LFC in power systems, capable of effectively handling network attacks and disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…It introduces a novel delay margin estimation technique to ascertain the maximum permissible delay for maintaining system stability, which enhances the control system's robustness compared to traditional methods. Using brain-inspired deep meta-reinforcement learning, reference [13] enhances multi-area grids' load frequency control (LFC). This approach develops a fault-tolerant LFC system that adapts to disturbances and faults, showing superior adaptability and robustness compared to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Load Frequency Control for Multi-Area Power System Based on Switching Method under Cyber Attacks

Tian,

Wang

2024

Algorithms

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This paper introduces an innovative method for load frequency control (LFC) in multi-area interconnected power systems vulnerable to denial-of-service (DoS) attacks. The system is modeled as a switching system with two subsystems, and an adaptive control algorithm is developed. Initially, a dynamic linear data model is used to model each subsystem. Next, a model-free adaptive control strategy is introduced to maintain frequency stability in the multi-area interconnected power system, even during DoS attacks. A rigorous stability analysis of the power system is performed, and the effectiveness of the proposed approach is demonstrated by applying it to a three-area interconnected power system.

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Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Load Frequency Control for Multi-Area Power System Based on Switching Method under Cyber Attacks

Tian,

Wang

2024

Algorithms

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“…Similarly, the active output of the photovoltaic power generation is modelled by simulating the daily variation in light intensity. Detailed parameters for each energy unit are provided in (Li et al, 2023a). This approach allows for a comprehensive analysis of the DAC-PPO's performance under varying and unpredictable energy inputs.…”

Section: Case 2: Step Disturbance and Renewable Disturbancementioning

confidence: 99%

“…Load frequency control (LFC) in islanded microgrids primarily focuses on generating power regulation commands based on frequency deviations to maintain frequency within optimal ranges, which is crucial for the safe and stable operation of these systems. Various LFC methods have been proposed by researchers, including proportional-integral control (Patel et al, 2020), model predictive control (Li et al, 2022), adaptive control (Naderipour et al, 2019), sliding mode control (Li et al, 2023a), fuzzy control (Deshmukh et al, 2020), and robust control (Li et al, 2023b). Nonetheless, given the highly nonlinear and rapid-response nature of islanded microgrids, these methods often struggle to achieve multi-objective optimal coordinated control in complex stochastic environments.…”

Section: Introductionmentioning

confidence: 99%

Generating adversarial deep reinforcement learning -based frequency control of Island City microgrid considering generalization of scenarios

Wang,

Zhang,

Wang

2024

Front. Energy Res.

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The increasing incorporation of new energy sources into power grids introduces significant variability, complicating traditional load frequency control (LFC) methods. This variability can cause frequent load disturbances and severe frequency fluctuations in island city microgrids, leading to increased generation costs. To tackle these challenges, this paper introduces a novel Data knowledge-driven load frequency control (DKD-LFC) method, aimed at optimizing the balance between generation cost and frequency stability in isolated microgrids with high renewable energy integration. The DKD-LFC replaces conventional controllers with agent-based systems, utilizing reinforcement learning for adaptive frequency control in complex environments. A new policy generation algorithm, based on generative adversarial-proximal policy optimization (DAC-PPO), is proposed. This algorithm extends the traditional Actor-Critic framework of the Proximal Policy Optimization (PPO) by incorporating a Discriminator network. This network evaluates whether the input state-action pairs align with current or expert policies, guiding policy updates toward expert policies during training. Such an approach enhances the algorithm’s generalization capability, crucial for effective LFC application in diverse operational contexts. The efficacy of the DKD-LFC method is validated using the isolated island city microgrid LFC model of the China Southern Grid (CSG), demonstrating its potential in managing the complexities of modern power grids.

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A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

Roy,

Debbarma,

Roy

et al. 2024

IET Energy Syst Integration

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The consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.

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Brain-Inspired Deep Meta-Reinforcement Learning for Active Coordinated Fault-Tolerant Load Frequency Control of Multi-Area Grids

Cited by 8 publications

References 23 publications

Data-Driven Load Frequency Control for Multi-Area Power System Based on Switching Method under Cyber Attacks

Data-Driven Load Frequency Control for Multi-Area Power System Based on Switching Method under Cyber Attacks

Generating adversarial deep reinforcement learning -based frequency control of Island City microgrid considering generalization of scenarios

A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

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