Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

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

doi:10.1007/s00521-016-2361-1

Cited by 110 publications

(38 citation statements)

References 27 publications

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“…Frequency response model of three-area thermal power systems are proposed in [65,66]. The LFC for three-area hydro power systems is marked out in [59,[67][68][69]. Load frequency controllers are designed for a hydro-thermal power system divided into three-areas [50,70].…”

Section: Three-area Power Systemsmentioning

confidence: 99%

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

et al. 2018

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Power systems are the most complex systems that have been created by men in history. To operate such systems in a stable mode, several control loops are needed. Voltage frequency plays a vital role in power systems which need to be properly controlled. To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems. Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance. Likewise, the power exchanges between different control areas are controlled by LFC approaches. In recent decades, many control approaches have been suggested for LFC in power systems. This paper presents a comprehensive literature survey on the topic of LFC. In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems. Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups. The paper concludes with highlighting the research gaps and presenting some new research directions in the field of LFC.Energies 2018, 11, 2497 2 of 35 before triggering the under/over frequency protection relays. The primary frequency control is usually implemented by the governor droop which results in steady stated errors. The secondary frequency control, which is called load frequency control (LFC) or automatic generation control (AGC), is responsible for regulating the frequency in power systems and has two main goals: (i) maintaining the frequency into a desirable range; and (ii) controlling the interchange power through major tie-lines between the different control areas. The main task of tertiary control level is re-dispatching generating units and ancillary reserve after a sever disturbance.With increasing the penetration level of renewable resources such as wind farms and photovoltaic plants in power systems, the uncertainties of active power production is highly increased, thus determining frequency variations. Such increase in active power fluctuation, beside the demand stochasticity, the frequency of power system would be highly oscillated. Therefore, future power systems need more robust and optimal LFC approaches that can handle such problems.Many control approaches have been suggested for LFC in interconnected power systems. These approaches can be categorized into four groups: (i) classical control approaches focus on designing proportional-integral-derivative (PID) controllers for controlling the frequency and tie-lines power flows; (ii) modern control approaches including optimal control method, sliding mode control schemes, and adaptive control systems; (iii) intelligent control schemes, such as fuzzy control systems; and (iv) soft computing-based approaches for controllers' parameter tuning which had a considerable attention from researchers in the last decade. Survey MethodologySeveral methodologies can be followed for conduc...

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Section: Three-area Power Systemsmentioning

confidence: 99%

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

et al. 2018

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“…To get the optimal value, velocity and position of particles is updated in successive iterations using Equations (13) and (14). The process continues till the stopping conditions is meet.…”

Section: Hybrid Molgsamentioning

confidence: 99%

“…In [10], the authors have studied the PID controller gains tuned by Imperialist Competitive Algorithm (ICA). Various novel technique/control approaches such as Firefly Algorithm (FA) optimized PI controller [11,12], Flower Pollination Algorithm (FPA) tuned PID controller [13], Grey Wolf Optimization (GWO) tuned classical controller with PI and PID structure [14], Differential Evolution (DE) tuned 2-DOF PID [15], TLBO tuned 2-DOF PID [16], ICA-based fuzzy-PI [17], hybrid FA and Pattern Search (hFA-PS) optimized PI and PID [18], A hybrid gravitational search algorithm (GSA) and PS tuned PI/PIDF controller [19], hybrid Stochastic Fractal Search and Local Unimodal Sampling (hSFS-LUS) optimized multistage PDF plus (1 + PI) [20] have been recently suggested for various types of power systems for frequency control.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid many optimizing liaisons gravitational search algorithm approach for AGC of power systems

2019

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A hybrid Many Optimizing Liaisons Gravitational Search Algorithm (hMOL-GSA)-based fuzzy PID controller is proposed in this work for Automatic Generation Control problem. MOL is a simplified version of particle swarm optimization which ignores the particle best position consequently simplifying the algorithm. The proposed method is employed to tune the fuzzy PID parameters. The outcomes are equated with some newly proposed methods like Artificial Bee Colony (ABC)-based PID for the identical test systems to validate the supremacy of GSA and proposed hMOL-GSA techniques. Further, the design task has been carried out in a three-area test system and the outcomes are equated with newly proposed Firefly Algorithm (FA) optimized PID and Teaching Learning-Based Optimization (TLBO) tuned PIDD controller for the identical system. Better system response has been observed with proposed hMOL-GSA method. Finally, sensitivity study is being carried out and robustness of the proposed method is established.

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“…Natural pollinators are simulated by using levy flights. Studies have revealed the ingenuity of the FPA algorithm over other metaheuristics [2,7,24,40]. Motivated by this, this algorithm has been employed in the current study in training the NNs of the HNN model.…”

Section: Metaheuristic Supported Neural Networkmentioning

confidence: 99%

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

Chatterjee¹,

Datta²,

Dey³

2018

NNW

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The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.

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Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

Cited by 110 publications

References 27 publications

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

A novel hybrid many optimizing liaisons gravitational search algorithm approach for AGC of power systems

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

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