Load frequency control problem in interconnected power systems using robust fractional PI λ D controller

Abdelmoumene, Delassi; Arif, Salem; Mokrani, L.

doi:10.1016/j.asej.2015.10.004

Cited by 83 publications

(59 citation statements)

References 17 publications

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“…In literature four different performance indices are used including ITSE, ITAE, IAE, and ISE. However, ITAE [12,16,17,32], ITSE [3,10,11,21] and ISE [14,18,20,29] are more frequently used for AGC problem. For the comparison among various performance indices Eq (5) to Eq (8) are implemented in matlab for two area multi-source IPS and achieved minimum fitness values for ITSE as compared to ITAE, ISE and IAE which is depicted in Table 1.…”

Section: A Controller Structurementioning

confidence: 99%

“…Authors in [13] proposed PID controller tuned with Salp Swarm Algorithm (SSA) for LFC of multi-area IPS including GRC, GDB and Communication Delay (CD) as physical constraints. Delassi et al [14] proposed fractional order fuzzy based PID controller using Differential Evolution (DE) technique considering three non linearities such as GRC, BD and GDB for LFC of three area with single source reheat-thermal unit. However, in literature multiple non linearities such as GDB, GRC, TD and BD have not been addressed collectively for multi area with multiple source power generation.…”

Section: Introductionmentioning

confidence: 99%

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Fitness Dependent Optimizer-Based Automatic Generation Control of Multi-Source Interconnected Power System With Non-Linearities

et al. 2020

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This paper proposed an improved structure of Proportional Integral Derivative (PID) controller called as Integral Proportional Derivative (I-PD), applied for Automatic Generation Control (AGC) of Multi-Source Interconnected Power System (IPS). The parameters of the proposed controller are optimized with a newly developed, powerful, nature-inspired meta-heuristic technique known as Fitness Dependent Optimizer (FDO). To show the efficacy of the proposed controller and the technique used, they have been tested on three different system models. Initially, a two-equal area of diverse source generation including reheat-thermal, gas, and hydro power system is considered. In the second scenario, the same power system model is used with addition of two non-linearities; Generation Rate Constraint (GRC) and Governor Dead Band (GDB). Lastly, multiple non-linearities including Governor Dead Band (GDB), Time Delay (TD), Generation Rate Constraint (GRC), and Boiler Dynamics (BD) have been considered to make the initial system more realistic and practical. The outcome from the proposed techniques is also compared with some recently meta-heuristic algorithms such as Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). From the results, it has been perceived that the proposed technique shows superior performance in respect of Overshoot (Osh), Undershoot (Ush) and Settling Time (Ts) of the system frequency.

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Section: A Controller Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fitness Dependent Optimizer-Based Automatic Generation Control of Multi-Source Interconnected Power System With Non-Linearities

et al. 2020

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“…Moreover, a high pass filter is equipped with the derivative term to reduce the effect of any input noise on system stability [28]. Moreover, the fractional order (FO) based controllers have been also presented in the literature including the fractional order PID (FOPID) [29], fuzzy fractional order PI and PD controller (FL-FOPI-FOPD) [30], fuzzy with FOPIDF (FL-FOPIDF) [31], combined fuzzy PIDF and FOI (FL-PIDF-FOI) [32], hybrid [27], etc. Hence, the family of PID controller, FO, and their extensions based expanded controllers are widely preferred in LFC thanks to their simplicity, robustness, and efficiency.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

et al. 2020

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Multi-area power systems inhere complicated nonlinear response, which results in degraded performance due to the insufficient damping. The main causes of the damping problems are the stochastic behavior of the renewable energy sources, loading conditions, and the variations of system parameters. The load frequency control (LFC) represents an essential element for controlling multi-area power systems. Therefore, the proper design of the controllers is mandatory for preserving reliable, stable and high-quality electrical power. The controller has to suppress the deviations of the area frequency in addition to the tie-line power. Therefore, this paper proposes a new frequency regulation method based on employing the hybrid fractional order controller for the LFC side in coordination with the fractional order proportional integral derivative (FOPID) controller for the superconducting energy storage system (SMES) side. The hybrid controller is designed based on combining the FOPID and the tilt integral derivative (TID) controllers. In addition, the controller parameters are optimized through a new application of the manta ray foraging optimization algorithm (MRFO) for determining the optimum parameters of the LFC system and the SMES controllers. The optimally-designed controllers have operated cooperatively and hence the deviations of the area frequency and tie-line power are efficiently suppressed. The robustness of the proposed controllers is investigated against the variation of the power system parameters in addition to the location and/or magnitude of random/step load disturbances.

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“…There are several linear and non-linear control techniques employed in LFC problem of power systems such as classical integral (I), proportional-integral (PI), and proportionalintegral-derivative (PID) controllers [3]- [13], PID controller with derivative filter (PIDF) [14], two degree of freedom PID controller (2-DOF PID) [15], [16], PID plus second order derivative controller (PID+DD) [17], PD-PID cascade controller [18], fractional order PID controller (FOPID) [19]- [24], fuzzy fractional order PI and PD controller (FFOPI-FOPD) [25], fuzzy logic based PID (FPID) and FOPID controllers with derivative filter (FFOPIDF) [26], fuzzy PID with filter and fractional order integer controller (FPIDN-FOI) [27], fuzzy tuned PI (FPI) and PID controllers [28], [29], fuzzy tuned fractional order integerderivative controller (FFOID) [30], tilt integral-derivative controller with derivative filter (TIDF) [31], neuro-fuzzy hybrid intelligent PI controller [32], linear active disturbance rejection control (LADRC) [33], LADRC controller with two anti-GDB schemes [34], and etc. Since, the intelligent and modern control techniques generally require long computational complexities like learning process, expert knowledge and inference mechanism, PID controller and its expanded versions are highly popular for LFC problem because of its two main advantages of simplicity and efficiency [35].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, meta-heuristic algorithms have a great attention in controller parameter tuning process, especially in PID controller design. Quasi-oppositional harmony search algorithm (QOHS) [3], chaos-based firefly algorithm (CFFA) [4], salp swarm algorithm (SSA) [5], craziness based particle swarm optimization (CRAZYPSO) [6], quasi-oppositional grey wolf optimization (QOGWO) [7], differential evolution (DE) [8], [16], [19], [31], firefly algorithm (FA) [9], [12], hybrid bacteria foraging optimization algorithm and particle swarm optimization algorithm (hBFOA-PSO) [10], bacterial foraging optimization algorithm (BFOA) [11], symbiotic organism search (SOS) [13], differential search algorithm (DSA) [14], teaching learning based optimization (TLBO) [15], ant lion optimizer algorithm (ALO) [17], bath algorithm (BA) [18], grasshopper optimization algorithm (GOA) [20], non-dominated sorting genetic algorithm-II (NSGA-II) [21], imperialist competitive algorithm (ICA) [22], [25], [27], [30], cuckoo optimization algorithm (COA) [26], COA into harmony search (HS) algorithm (HSCOA) [28], hybrid local unimodal sampling (LUS) and TLBO (LUS-TLBO) [29], and gravitational search algorithm (GSA) [33] are some of the meta-heuristic and hybrid metaheuristic optimization algorithms employed to set PID controller parameters used in LFC system. The performances and advantages of the optimization algorithms are generally compared to well-known optimization algorithms such as PSO and GA.…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimized Fractional High Order Differential Feedback Controller for Load Frequency Control of a Multi-Area Multi-Source Power System With Nonlinearity

Şahin

2020

IEEE Access

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Load frequency control (LFC) is one of the essential process in interconnected power systems. To provide high quality, reliable and stable electrical power, designed controller should perform satisfactorily, i.e. suppress area frequency and tie-line power deviations. Within this scope, in this study, a high order differential feedback controller (HODFC) and a developed fractional high order differential feedback controller (FHODFC) are proposed for LFC problem in multi-area power systems for the first time. The gains of the HODFC and FHODFC are optimally tuned by particle swarm optimization (PSO) algorithm aiming to minimize integral of time weighted absolute error (ITAE) performance index. The superiority of the developed FHODFC are verified by comparing reported controller structures in the recent stateof-the-art literature and HODFC for two identical non-reheat thermal power system and two-area multisource power system consisting of gas, thermal and hydro generation units with/without consideration of HVDC link. To test the robustness of the designed controllers, varying system parameters and loading conditions are investigated. The governor dead band (GDB) and generation rate constraint (GRC) limitations are also considered for the system under study to examine non-linearity handling success of the proposed controllers. Performance results indicate that the developed FHODFC provides better dynamic response and robustness than other published techniques under nonlinearities, random load pattern, and variations in system parameters and loading conditions. INDEX TERMS Load frequency control, governor dead band, generation rate constraint, random load pattern, fractional calculus, fractional high order differential feedback controller, particle swarm optimization, robustness and transient response analysis.

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Load frequency control problem in interconnected power systems using robust fractional PI λ D controller

Cited by 83 publications

References 17 publications

Fitness Dependent Optimizer-Based Automatic Generation Control of Multi-Source Interconnected Power System With Non-Linearities

Fitness Dependent Optimizer-Based Automatic Generation Control of Multi-Source Interconnected Power System With Non-Linearities

An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

Design of an Optimized Fractional High Order Differential Feedback Controller for Load Frequency Control of a Multi-Area Multi-Source Power System With Nonlinearity

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