Power System Stability Improvement through the Coordination of TCPS-based Damping Controller and Power System Stabilizer

Ali, Muhammad Arif Sharafat; Mehmood, Khawaja Khalid; Kim, Chul‐Hwan

doi:10.4316/aece.2017.04004

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…Therefore, research on safe and stable power system operations with the massive integration of wind power plants (WPPs) has garnered significant attention [1,2]. Furthermore, evolving power systems are equipped with advanced control structures to ensure reliable and quality operations [3][4][5][6]. Grid synchronization is significant for aligning highly stable system operations with wind-driven doubly-fed induction generators (DFIGs).…”

Section: Introduction 1backgroundmentioning

confidence: 99%

LMI-Based State Feedback Control Structure for Resolving Grid Connectivity Issues in DFIG-Based WT Systems

Ali¹

2021

Eng

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Pertaining to the connectivity issues in wind power plants with grids, this study introduces an efficient mechanism based on a state feedback control structure to establish a fast and stable grid connection for a wind-driven doubly-fed induction generator (DFIG). Owing to a direct link through stator windings, a DFIG is significantly vulnerable to grid disturbances and experiences sizable inrush currents when connected to the power grid. The proposed control structure is designed based on a linear matrix inequality stabilization criterion, which is framed using a suitable Lyapunov candidate function. The control objective is to ensure that the stator voltage can exponentially converge to the grid voltage, accounting for balanced and unbalanced grid conditions. This is achieved by generating appropriate rotor voltage references for rotor-side converter control. This study also explores the capability of the proposed control structure to enhance the system’s robustness to external disturbances and uncertain parametric variations. Simulations confirmed the effectiveness and suitability of the developed structure in mitigating the adverse effects of the rapid onset of the grid voltage at the stator terminals of the DFIG under various grid conditions; the proposed structure could thus establish a reliable connection with negligible effects on the DFIG and the grid.

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Section: Introduction 1backgroundmentioning

confidence: 99%

LMI-Based State Feedback Control Structure for Resolving Grid Connectivity Issues in DFIG-Based WT Systems

Ali¹

2021

Eng

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“…Power generation companies always innovate to ensure that the energy supply is sufficient and stable. Accordingly, small signal stability is a frequently discussed topic, as reported in [1][2][3][4][5]. This system must be tracked online because its power system operating condition changes over time.…”

Section: Introductionmentioning

confidence: 99%

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

et al. 2020

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This study discusses the evaluation of oscillatory stability based on the synchronizing K s and damping K d torque coefficients for a single-machine system connected to an infinite bus (SMIB). Particle swarm optimization (PSO) technique is used to determine K s and K d values and subsequently identify the oscillatory stability conditions in the SMIB. The ability of PSO is compared with those of evolutionary programming (EP) techniques and artificial immune system (AIS). The least square (LS) method is selected as the benchmark for K s and K d values determined by PSO, EP, and AIS. Simulation results show that PSO successfully estimated K s and K d values closest to LS compared with EP and AIS. PSO also uses lower computational time compared with those of the two other techniques. The proposed technique is suitable for solving oscillatory stability problems based on the determination of eigenvalues and minimum damping ratio.Energies 2020, 13, 1231 2 of 15 K d values. Thus, heuristic techniques are introduced to solve this problem. K s and K d values will be optimized from the beginning of the data until constant K s and K d values are obtained. Therefore, only a portion of the data is necessary for estimating K s and K d . Minor data errors do not significantly affect the determination of K s and K d values.In recent years, the use of artificial intelligence (AI) technology has become the preferred option in solving power system problems. The use of AI is introduced to solve the optimum values of a system or condition particularly in the fields of economic dispatch, capacitor placement and sizing, and assessment and improvement of voltage and oscillatory stability. Artificial neural networks [16,17], evolutionary programming (EP) [18][19][20], artificial immune systems (AIS) [21][22][23], and ant colony optimization (ACO) [24][25][26] are AI approaches that are commonly used in power systems. The EP algorithm is modeled on the biological evolution process of solving a complex problem. The main features of EP include the mutation process of the next generation and the selection of increasingly powerful genes. The AIS algorithm uses a concept similar to that of EP. Although both concepts are biologically based on living things, EP focuses on the evolution of living things, whereas AIS adopts the concept of the living immune system. The difference between AIS and EP algorithms is that AIS has an additional process of cloning called the clonal selection algorithm. However, the ACO approach is inspired by the true behavior of ants while searching for food and interacting with fellow ants. In ACO, artificial ants (the search agent) will communicate by using pheromones, which guide the searcher ants to solve the calculation problem by tracking the best route. Meanwhile, the particle swarm optimization (PSO) [27][28][29][30] concept mimics the movements of a herd, such as the behavior of schooling fish and swarming insects. This technique was originally founded based on the population of random particles, in which every p...

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“…Since the damping characteristic of the power system is weakened as the interconnection of power grids, it is desirable to enhance the system damping characteristic by designing the damping controllers (Ali et al, 2017;Khaki et al, 2011;Khaki et al, 2011;Li & Chen, 2018;Mohammadi et al, 2016;Ngamroo, 2013;Yu et al, 2018). However, there is a limit to which the controller CONTACT Hong-Liang Gao gaohl2016@hbnu.edu.cn can improve the damping characteristic of a power system, and the global system stability may deteriorate due to the lack of coordination.…”

Section: Introductionmentioning

confidence: 99%

Comparison study of DLOEC and H_Infinity control strategies for transient stability enhancement of power system

Gao

2020

Systems Science & Control Engineering

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This paper studies the control strategies for enhancing the transient stability of a single machine infinite bus (SMIB) power system. First, the dynamical SMIB model is established, and the excitation control is discussed. Next, the damping linear optimal excitation controller (DLOEC) and the output feedback H_infinity control strategy are studied. Then, the DLOEC and the suboptimum H_infinity controller are designed for a specific SMIB power system. The effectiveness of the two presented methods is verified by simulations. The simulation results show that the presented methods improve the transient stability of the SMIB power system and that the H_infinity controller provides better performance than the DLOEC controller.

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Power System Stability Improvement through the Coordination of TCPS-based Damping Controller and Power System Stabilizer

Cited by 12 publications

References 31 publications

LMI-Based State Feedback Control Structure for Resolving Grid Connectivity Issues in DFIG-Based WT Systems

LMI-Based State Feedback Control Structure for Resolving Grid Connectivity Issues in DFIG-Based WT Systems

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

Comparison study of DLOEC and H_Infinity control strategies for transient stability enhancement of power system

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