A new coordination strategy of SSSC and PSS controllers in power system using SOA algorithm based on Pareto method

Gholipour, Eskandar; Nosratabadi, Seyyed Mostafa

doi:10.1016/j.ijepes.2014.12.020

Cited by 46 publications

(24 citation statements)

References 33 publications

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“…Recently, several newly proposed soft computing algorithms have been developed for LFC problem in both conventional and modern power systems. For instance, differential evolution (DE) algorithm [170,[276][277][278][279], firefly algorithm (FA) [173,280,281], bacterial foraging optimization (BFO) [282,283], artificial bee colony (ABC) [174,284], bat inspired algorithm (BIA) [285][286][287], quasi oppositional (QO) [54], quasi-oppositional harmony search (QOHS) algorithm [288,289], teaching-learning-based optimization (TLBO) [290], cuckoo search (CS) algorithm [291,292], seeker optimization algorithm (SOA) [293], hybrid Local Unimodal Sampling and Teaching Learning Based Optimization (LUSTLBO) algorithm [178], grey Wolf Optimizer algorithm [294], and wind driven optimization algorithm [295] have been applied to LFC in interconnected power systems. Other evolutionary computing algorithms applied to LFC can be found in Table 3.…”

Section: Soft Computing Based Control Schemesmentioning

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: Soft Computing Based Control Schemesmentioning

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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“…Yogendra Arya [18] suggested Imperialist Competitive Algorithm (ICA) for AGC of multi-area with hydro-thermal power units including GRC, GDB and TD as a non linearities. Similarly, some of the other techniques have also been introduced by the authors to solve the AGC problem of IPS such as Grey Wolf Optimizer (GWO) [19], Cuckoo Search Algorithm (CSA) [20], Artificial Bee Colony (ABC) [21], Symbiotic Organisms Search (SOS) [22], Quasi-Oppositional Harmony Search (QOHS) [23], Whale Optimization Algorithm (WOA) [24], and Seeker Optimization Algorithm (SOA) [25]. Moreover, some of the authors also used the hybridized form of the meta-heuristics techniques like hybrid FA with Pattern Search (FA-PS) [26], Bacteria Foraging Optimization Algorithm with PSO (BFOA-PSO) [27], PSO hybridized with Levy Flight Algorithm (PSO-LFA) [28], hybrid TLBO with Pattern Search (TLBO-PS) [29], TLBO hybrid with Local Unimodal Sampling (LUS-TLBO) [30] and hybridization of DE-GWO [31].…”

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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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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“…Moreover, use of a suitable heuristic algorithm has found to take more research space. In Gholipour and Nosratabadi, better system stability and oscillation damping is achieved by a coordinate structure of PSS and SSSC. Coordinated structure combining SSSC and PSS have profound application in the existing literature of lead‐lag controller.…”

Section: Introductionmentioning

confidence: 99%

Modified whale optimization algorithm for coordinated design of fuzzy lead‐lag structure‐based SSSC controller and power system stabilizer

Sahu

Hota

Panda

2018

Int Trans Electr Energ Syst

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Summary In this paper, a new fuzzy lead‐lag controller for a coordinated structure combining static synchronous series compensator and power system stabilizer is designed to improve the power system stability. The controller parameters are optimized by using a modified whale optimization algorithm (MWOA). The newly proposed MWOA attains a suitable balance among exploration and exploitation phases of WOA. This potential of this MWOA is certified by means of utilizing the benchmark functions and by further comparing with that of a few heuristic algorithms such as whale optimization algorithm, fast evolutionary programming algorithm, gravitational search algorithm, differential evolution, and particle swarm optimization. Here, the MWOA technique is utilized to tune the parameters of the proposed controller. The controller is tested under various loading conditions as well as system configurations such as single‐machine infinite bus system and multimachine power system. Finally, the simulation results so obtained using the designed controller under different disturbance conditions are contrasted with that of the conventional lead‐lag structure controller.

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A new coordination strategy of SSSC and PSS controllers in power system using SOA algorithm based on Pareto method

Cited by 46 publications

References 33 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

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

Modified whale optimization algorithm for coordinated design of fuzzy lead‐lag structure‐based SSSC controller and power system stabilizer

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