Optimal load frequency control through combined state and control gain estimation for noisy measurements

Abstract

Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise.

Frequency emergency control strategy in power systems considering the participation of energy storage clusters

“…With the vigorous development of renewable energy generation and the integration of a large number of power electronic devices [such as high-voltage direct current (HVDC) transmission systems], the power systems with a high penetration of renewables and power electronics have come into being (Xu et al, 2022;Yang et al, 2022;Zhu et al, 2022). In such power systems, the complex system dynamics have brought new challenges to the system operation and control (Pillai et al, 2020;Yang et al, 2021a). This paper focuses on the frequency control in the power systems with a high penetration of renewables and power electronics.…”

Solar and Wind Energy Integrated System Frequency Control: A Critical Review on Recent Developments

“…The given two-area system is trained using multiple deep reinforcement learning agents to reduce frequency fluctuation and tie-line power oscillation. Some other control approaches such as model predictive control [23,24], fuzzy-logic control [25], non-linear control [26], optimal control [27], soft computing based control [28] are also implemented to control the frequency of the solar and wind system.…”

Multi-Objective Mayfly Optimization-Based Frequency Regulation for Power Grid With Wind Energy Penetration

“…However, wind power generation is vulnerable to weather, resulting in large power fluctuations (Li et al, 2020). At present, the research on the participation of renewable energy in AGC is mainly about the design of controller and gain optimization, and the coordinated operation of renewable energy and traditional hydro/thermal power units is not considered (Suresh Kumar et al, 2017; Nizamuddin et al, 2018;Yogendra, 2018;Celik, 2020;Pillai et al, 2020;An and Nishat, 2021;Arya et al, 2021;Gaber et al, 2022).…”