Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm

Jagatheesan, K.; Anand, B.; Samanta, Sourav; Dey, Nilanjan; Ashour, Amira S.; Balas, Valentina E.

doi:10.1109/jas.2017.7510436

Cited by 152 publications

(63 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At present, many AI methods have been proposed to autotune the PID parameter, such as fuzzy logic [2], [10], [19], [20], neural networks (NNs) [1], [7], [21], particle swarm optimization (PSO) algorithms [6], [22], [23], hybrid firefly (FA) and pattern search [8], the ant lion optimization (ALO) algorithm [24], the whale optimization algorithm (WOA) [25], cuckoo search (CS) [10], [26], bacterial foraging optimization [27], genetic algorithms [28], the cosine algorithm [29], the bat algorithm [12], ant colony optimization (ACO) [13], [30], differential evolution (DE) [31], World Cup optimization (WCO) [32], evaluation algorithms (EAs) [33], [34], gray wolf optimization (GWO) [35], nature-inspired algorithms [17], chaotic invasive weed optimization [36], [37], flower pollination algorithm (FPA) [38] and firefly algorithm (FFA) [39]. Although many AI methods have been proposed to autotune the PID parameter, the challenges of long execution time and convergence persist.…”

Section: Introductionmentioning

confidence: 99%

PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

Pongfai

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The proportional integral and derivative (PID) controller is extensively applied in many applications. However, three parameters must be properly adjusted to ensure effective performance of the control system: the proportional gain (K P), integral gain (K I) and derivative gain (K D). Therefore, the aim of this paper is to optimize and improve the stability, convergence and performance in autotuning the PID parameter by using a deterministic Q-SLP algorithm. The proposed method is a combination of the swarm learning process (SLP) algorithm and Q-learning algorithm. The Q-learning algorithm is applied to optimize the weight updating of the SLP algorithm based on the new deterministic rule and closed-loop stabilization of the learning rate. To validate the global optimization of the deterministic rule, it is proven based on the Bellman equation, and the stability of the learning process is proven with respect to the Lyapunov stability theorem. Additionally, to demonstrate the superiority of the performance and convergence in autotuning the PID parameter, simulation results of the proposed method are compared with those based on the central position control (CPC) system using the traditional SLP algorithm, the whale optimization algorithm (WOA) and improved particle swarm optimization (IPSO). The comparison shows that the proposed method can provide results superior to those of the other algorithms with respect to both performance indices and convergence. INDEX TERMS Autotuning gain, central position control system, Q-learning algorithm, PID controller, swarm learning process algorithm, optimal control.

show abstract

Section: Introductionmentioning

confidence: 99%

PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

Pongfai

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…If this mismatch is hampered, it leads to deviations in tie‐power and frequency from actual values . Many research articles are available on AGC in the past . The early works on AGC started with single‐area thermal system and further extended to multiarea thermal systems.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional method of parameter tuning will be laborious and leads to suboptimum results. Several computational algorithms like bacterial foraging algorithm, grey wolf optimization, cuckoo search algorithm, firefly algorithm, flower pollination algorithm, and whale optimization technique are used in AGC studies, which lead to global optima. All the above algorithms were utilized successfully in AGC studies for optimization of controller gains and other parameters.…”

Section: Introductionmentioning

confidence: 99%

AGC of a multiarea system incorporating accurate HVDC and precise wind turbine systems

Babu

Saikia

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

This article highlights the dynamic model of an accurate high‐voltage direct current (AHVDC) using inertia emulation control strategy of a transmission line in automatic generation control (AGC) of a multiarea different source system. The three‐area system composes of thermal and precise wind turbine system using fixed and random wind velocities in areas 1 and 2. Area 3 composes of thermal and solar thermal power plants. Governor dead band and appropriate generation rate constraints are considered for thermal systems. A new fractional‐order cascaded controller named as fractional‐order proportional‐integral cascaded with fractional‐order integral derivative with filter (FOPI‐FOIDN) is used as a secondary controller. The controller gains and other parameters are optimized by using crow search algorithm in AGC studies for the first time. Comparison of system dynamics with various fractional‐order controllers and proposed FOPI‐FOIDN controller while considering one at a time reveals better dynamic performance of the latter. Comparison of dynamic response of the system with AC tie‐line, AHVDC tie‐line, and parallel combination of AC‐AHVDC tie‐lines while considering one at a time reveals better performance of the latter. The optimum location of AHVDC tie‐line in parallel AC tie‐line is found in the area where the system is disturbed.

show abstract

“…Firefly algorithm and its modified forms were also successfully applied to numerous practical problems. Jagatheesan et al used firefly algorithm to design a controller for an automatic generation control of multiarea power thermal systems [ 16 ]. An FA-inspired band selection and optimized extreme learning machine were proposed for hyperspectral image classification [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior

Peng

Dong

Yin

2018

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Artificial fish swarm algorithm easily converges to local optimum, especially in solving the global optimization problem of multidimensional and multiextreme value functions. To overcome this drawback, a novel fish swarm algorithm (LFFSA) based on Lévy flight and firefly behavior is proposed. LFFSA incorporates the moving strategy of firefly algorithm into two behavior patterns of fish swarm, i.e., chasing behavior and preying behavior. Furthermore, Lévy flight is introduced into the searching strategy. To limit the search band, nonlinear view and step size based on dynamic parameter are considered. Finally, the proposed algorithm LFFSA is validated with several benchmark problems. Numerical results demonstrate that LFFSA has a better performance in convergence speed and optimization accuracy than the other test algorithms.

show abstract

Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm

Cited by 152 publications

References 0 publications

PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

AGC of a multiarea system incorporating accurate HVDC and precise wind turbine systems

Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior

Contact Info

Product

Resources

About