Load frequency control of a dynamic interconnected power system using generalised Hopfield neural network based self‐adaptive PID controller

Ramachandran, Rajeswari; Balasubramonian, M.; Veerasamy, Veerapandiyan; Saravanan, Loheswaran

doi:10.1049/iet-gtd.2018.5622

Cited by 54 publications

(31 citation statements)

References 28 publications

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“…The multi-area HPS model shown in Figure 1 consists of reheat thermal system with associated system nonlinearities such as GDB and GRC, RE sources such as WTPG, STPG, AE, FC and PEV. The detail explanation of thermal power generation and its parameter for simulation is presented in [7], [40]. Further, the various RE model are described in detail [41] as follows;…”

Section: Power System Modelmentioning

confidence: 99%

“…To overcome this, the fuzzy and AI based techniques were used to tune the gain parameters of controller for ALFC application. Although the design of FL and NN based classical controllers have self-adaptive properties to handle non-linearities in dynamical systems, their performance degrades if the network is not properly designed [7]- [9]. Numerous factors such as membership function, fuzzy rules, transfer function model, number of input and output layers and method of training the network can affect the output of the designed controller.…”

Section: Introductionmentioning

confidence: 99%

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A Matignon’s Theorem Based Stability Analysis of Hybrid Power System for Automatic Load Frequency Control Using Atom Search Optimized FOPID Controller

et al. 2020

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The large-scale penetration of intermittent renewable energy (RE) sources such as wind and solar power generation may cause a problem of frequency aberration, power quality and instability to the system. This occurs when the load frequency control of interconnected system is unable to compensate the power balance between generation and load demand. To overcome these issues, this paper proposes a method to analyze the frequency stability of Hybrid Power System (HPS) through adaptive intelligent control techniques for delivering reliable power supply under the stochastic nature of RE and load demand. A recently developed physics-inspired Atom Search Optimization algorithm was applied for tuning the parameters of Fractional Order Proportional-Integral-Derivative controller for Automatic Load Frequency control of HPS. In addition, an effort was made to analyze the frequency stability of HPS using Matignon's theorem. The interconnected HPS consists of reheat thermal power system, RE sources such as wind and solar thermal power generation associated with energy storage devices namely aqua electrolyzer, fuel cell and electric vehicle. The gain and fractional terms of the controller were obtained by minimizing the Integral Time Absolute Error of interconnected system. The robustness of ASO-tuned FOPID controller is tested on two-area HPS that was modelled using MATLAB/Simulink. The results obtained were then compared with other fractional order and classical integer order controllers. From the simulation results, it is inferred that the proposed ASO-tuned FOPID controller gives superior transient and steady-state response compared with other controllers. Moreover, the self-adaptiveness and robustness of the controller was validated to account for the change in RE power generations and system parameters. Furthermore, the effectiveness of the method is proved by comparing its performance with the recent literature works. The real-time applicability of proffered controller is validated in hardware-in-the-loop simulation using Real Time Digital Simulator.

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Section: Power System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Matignon’s Theorem Based Stability Analysis of Hybrid Power System for Automatic Load Frequency Control Using Atom Search Optimized FOPID Controller

et al. 2020

Self Cite

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“…A variety of significant achievements have been made for the LFC task in the power systems using wellknown control theories. Recently, advanced control techniques have attracted the attention of more researches, such as robust control theories [6][7][8], model predictive control [6,[9][10][11][12], sliding mode control [13], neural network control [14][15][16]. The majority of relevant literature merely concentrate on frequency regulation and optimisation of control action, but the impact of protection system is overlooked in most of cases.…”

Section: Introductionmentioning

confidence: 99%

Emergency wind power plant re‐dispatching against transmission system cascading failures using reverse tracking of line power flow

Hoseinzadeh

Blaabjerg

Chen

et al. 2020

IET Generation, Transmission & Distribution

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“…Mehrnoush et al (2018) corrected the PID controller based on the fuzzy wavelet neural network model [10]. Ramachandran et al (2018) used a PID controller based on the generalized Hopfield neural network to control the load frequency of the power system [11]. Thus, it proves that neural networks based on deep learning have outstanding control capabilities for nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Sun

2020

PLoS ONE

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The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID. The controllers and hybrid bearing system are controlled based on the RBF neural network based on deep learning. The characteristics of the hybrid bearing system are explored at the end of this study, and the control simulation research is developed based on the Simulink simulation platform. The effects of the PID, IPD, and CIPD controllers based on the RBF neural network are compared, and they are also compared based on the traditional particle swarm optimization (PSO). The results show that the thickness, spread angle, and rotation speed of the elastic foil have great impacts on the bearing system. The proposed CIPD bearing control method based on RBF neural network has the shortest response time and the best control effect. The controller parameter tuning optimization starts to converge after one generation, which is the fastest iteration. It proves that RBF neural network control based on deep learning has high feasibility in hybrid bearing system. Therefore, the results provide an important reference for the application of deep learning in rotating machinery.

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Load frequency control of a dynamic interconnected power system using generalised Hopfield neural network based self‐adaptive PID controller

Cited by 54 publications

References 28 publications

A Matignon’s Theorem Based Stability Analysis of Hybrid Power System for Automatic Load Frequency Control Using Atom Search Optimized FOPID Controller

A Matignon’s Theorem Based Stability Analysis of Hybrid Power System for Automatic Load Frequency Control Using Atom Search Optimized FOPID Controller

Emergency wind power plant re‐dispatching against transmission system cascading failures using reverse tracking of line power flow

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

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