Implementation of hybrid SSA–SA based three‐degree‐of‐freedom fractional‐order PID controller for AGC of a two‐area power system integrated with small hydro plants

Nayak, Jyoti Ranjan; Shaw, Binod; Sahu, Binod Kumar

doi:10.1049/iet-gtd.2019.0113

Cited by 52 publications

(21 citation statements)

References 27 publications

(51 reference statements)

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“…Moreover, in [6], [22]- [24], the various FO-based controllers have been presented and compared for solving LFC issues. The comparison included the PID, FOPID, two-degree-of-freedom (2DOF)-PID, 2DOF-FOPID, 3DOF-PID, 3DOF-FOPID, and CF-FOIDF.…”

Section: B Literature Reviewmentioning

confidence: 99%

Frequency Regulation of Electric Vehicle-Penetrated Power System Using MPA-Tuned New Combined Fractional Order Controllers

et al. 2021

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Energy transition from traditional generation sources into new renewable energy generation has become essential for facing climate changes. However, increased penetration levels of renewable energy sources (RESs) make power systems subjected to low inertia problems. Moreover, the continuously growing numbers of electric vehicles (EVs) have made the substantial need for their contribution in power systems. Therefore, this paper proposes a combined fractional-order controller using the parallel combination of tiltintegral-derivative with filter (TIDF) and hybrid fractional-order (HybFO) controllers for robust frequency regulation in interconnected power systems. The proposed controller is advantageous in combining the merits of two fractional-order controllers that result in more robust and effective load frequency control (LFC) at wide range and different types of disturbances. Furthermore, a new application of marine predator optimization algorithm (MPA) is proposed for simultaneously determining the optimum controller parameters in the different power system areas. The existing EVs contribute in performing additional functionality in power systems. EVs help in damping out the frequency and tie-line power oscillations in the proposed work. The two-area interconnected power system is selected as a case study with the installed photovoltaic (PV), and wind generations in addition to distributed EVs among areas. The obtained results show the superiority and suitability of the proposed controller over the traditional controllers in the literature. Additionally, the effectiveness of the MPA is validated and compared with recent meta-heuristic optimization algorithms.INDEX TERMS Electric vehicles (EVs), interconnected power systems, load frequency control (LFC), marine predator optimization algorithm (MPA), renewable energy sources (RESs).

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Section: B Literature Reviewmentioning

confidence: 99%

Frequency Regulation of Electric Vehicle-Penetrated Power System Using MPA-Tuned New Combined Fractional Order Controllers

et al. 2021

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“…The controller performance becomes more flexible by adding adjustable parameters  and  . The fractionalorder controller has better nonlinear characteristics in the HTRS [39]. The fractional Riemann-Liouville integral is defined as…”

Section: ) Fractional-order State Error Feedback Lawmentioning

confidence: 99%

Nonlinear Fractional Active Disturbance Rejection Speed Control for Stabilization of Hydraulic Turbine Regulating Systems With Mechanical Delay

Liu

Zhang

et al. 2021

IEEE Access

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An improved fractional-order nonlinear active disturbance rejection speed control (FO-NADRC) method is proposed for hydraulic turbine regulating systems (HTRSs) with a mechanical delay. First, the mathematical model of an HTRS with a mechanical time delay is established. Based on the principle of coordinate transformation, the state space equation of an HTRS with a time delay is transformed into the controllable normative mathematical model. Second, a new nonlinear function is proposed for the extended state observer (ESO) that improves the observation accuracy and suppresses the high-frequency oscillation of the HTRS. Third, a new fractional-order state error feedback law (FO-SEFL) is proposed by introducing double adjustable parameters. Fourth, according to the improved ESO and the FO-SEFL, a novel FO-NADRC is designed for the HTRS. Furthermore, the Popov-Lyapunov robust stability analysis method is used to analyze the stability of the hydraulic turbine regulating control systems with mechanical delay. Finally, numerical simulation experiments demonstrate the effectiveness and superiority of the proposed control scheme.

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“…The results indicate that the proposed controller has fewer frequency variations. In [19], hybrid salp swarm algorithm-simulated annealing (HSSA-SA) algorithm is introduced to enhance the proficiency of PID controller by sensibly plucking the gain parameters, and the supremacy of this method is substantiated.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations

Wang

Deng

et al. 2021

IET Renewable Power Gen

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Recently, solar power generation systems are more and more popular and widely used in grid connected power generation, intelligent buildings, and power supply in remote areas. For photovoltaic panels, due to the influence of factors such as light intensity and ambient temperature, their output voltage and current become uns, and the output voltage of a single photovoltaic panel is considerably low. As a result, Boost circuits are needed for voltage boosting. PID controller is commonly used for Boost converter because it can effectively control the controlled object according to the characteristics of the controlled object. However, when the controlled object is complex and variable, the appropriate parameters are hardly to be selected by experience, and the fixed controller parameters may lead to unexpected performances under different working conditions. Here, a genetic algorithm combined with BP neural network PID control (GA‐BPPID) is proposed to improve both dynamic and anti‐interference performances of Boost circuit by introducing the global optimization ability of genetic algorithm and the adaptive adjustment characteristics of BP neural network. System modelling and detailed controller design procedures are provided. Finally, the theoretical analysis and controller design are validated by simulation results.

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Implementation of hybrid SSA–SA based three‐degree‐of‐freedom fractional‐order PID controller for AGC of a two‐area power system integrated with small hydro plants

Cited by 52 publications

References 27 publications

Frequency Regulation of Electric Vehicle-Penetrated Power System Using MPA-Tuned New Combined Fractional Order Controllers

Frequency Regulation of Electric Vehicle-Penetrated Power System Using MPA-Tuned New Combined Fractional Order Controllers

Nonlinear Fractional Active Disturbance Rejection Speed Control for Stabilization of Hydraulic Turbine Regulating Systems With Mechanical Delay

Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations

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