The Augmented Unscented H-infinity Transform with H-infinity Filtering for Effective Wind Speed Estimation in Wind Turbines

Owen, Erica; Pieper, J.K.

doi:10.1109/epec52095.2021.9621395

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…These methods include state estimation techniques like Kalman filtering [1], Extended and Unscented Kalman Filter [16,17], data-driven modeling and system identification approaches [18], reinforcement learning based control [6,9,19,20], fuzzy logic control for rule-based adaptability [1,[21][22][23], and signal processing methods such as the wavelet transform [24]. Among these diverse methodologies, H-infinity (H∞) control stands out as a control theory approach that seeks to design controllers to minimize the worst-case effects of uncertainty and disturbances in a system [25][26][27]. In the context of Load Frequency Control, H∞ control plays a crucial role in achieving robust and optimal regulation of the power system's frequency and tie-line power flow while accounting for uncertainties and disturbances [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Among these diverse methodologies, H-infinity (H∞) control stands out as a control theory approach that seeks to design controllers to minimize the worst-case effects of uncertainty and disturbances in a system [25][26][27]. In the context of Load Frequency Control, H∞ control plays a crucial role in achieving robust and optimal regulation of the power system's frequency and tie-line power flow while accounting for uncertainties and disturbances [25][26][27][28][29]. However, these proposed methods in the literature to increase the control quality of RES integrated power systems exhibit various limitations.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

Sekyere,

Effah,

Okyere

2024

J. Electron. Electric. Eng.

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The constant rise in energy demand and concerns about climate change have led to increased penetration of renewable energy sources (RES). Maintaining active power balance between generation and demand in power systems with significant penetration of these highly variable and intermittent renewable sources requires an efficient load frequency control (LFC) strategy. One such strategy that has gained the attention of researchers is optimal tuning of PID controllers of LFC using metaheuristic method. This paper presents a PSO variant for optimal tuning of PID controllers for load frequency control of power system integrated with renewable energy resources. The proposed PID tuning technique is tested on a two-area power system commonly used in the literature. Seven scenarios have been used to validate the effectiveness of the proposed Load Frequency Control. For more realistic evaluation, governor dead band and communication time delays have been incorporated in the test system in one of the scenarios. Simulation results obtained when compared with those of three well-known PID-tuning metaheuristic algorithms produced shorter settling time and smaller frequency and tie line power deviations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

Sekyere,

Effah,

Okyere

2024

J. Electron. Electric. Eng.

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Neural algorithm for optimization of multidimensional object controller parameters

Bałazy,

Lalik,

Knap

2024

Neural Comput & Applic

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Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R, have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics.

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Application of an Augmented Unscented H-infinity Effective Wind Speed Estimation to H-infinity Control of Wind Turbines

Owen¹,

Pieper

2021

2021 IEEE Electrical Power and Energy Conference (EPEC)

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The Augmented Unscented H-infinity Transform with H-infinity Filtering for Effective Wind Speed Estimation in Wind Turbines

Cited by 5 publications

References 24 publications

Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

Neural algorithm for optimization of multidimensional object controller parameters

Application of an Augmented Unscented H-infinity Effective Wind Speed Estimation to H-infinity Control of Wind Turbines

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