Quasi-oppositional symbiotic organism search algorithm applied to load frequency control

Guha, Dipayan; Roy, Provas Kumar; Banerjee, Subrata

doi:10.1016/j.swevo.2016.10.001

Cited by 86 publications

(51 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The optimal DMPI controller parameters with QOWOA, WOA, MFO, and QOSOS, SOS, CLPSO, and hBFOA‐PSO techniques tuned PI controller with ITAE, ITSE, and ISE as objective function are specified in Table . As perceived from Table , a minimum value of ITAE, ISE, and ITSE is obtained with the QOWOA tuned DMPI controller; especially, improvement in ITAE 19.7% (WOA), 56% (MFO), 54.70% (QOSOS), 56.83% (SOS), 59.25% (KHA), 58.75% (CLPSO), and 80.49% (hBFOA‐PSO) is obtained with QOWOA tuned DMPI controller with nonlinearity. ST, OS, and US of the system with the abovementioned algorithms are presented in Table .…”

Section: Results and Analysismentioning

confidence: 99%

“…The optimal controller parameters along with error criteria values are given in Table 2a for the above load change. As depicted from Table 2a, the minimum value of ITAE, ITSE, and ISE is obtained with the QOWOA tuned PI controller compared with original WOA and MFO technique and other techniques QOSOS, 33 SOS, 33 KHA, 33 CLPSO, 33 hBFOA-PSO, 37 PSO, 37 BFOA, 18 and GA. 18 The improvement in ITAE objective function 1.84% (WOA), 40.91% (MFO), 37.36% (QOSOS), 46.88% (SOS), 37.84% (KHA), 62.83% (CLPSO), 90.29% (hBFOA-PSO), 78.82%(BFOA), and 93.09%(GA) is obtained with QOWOA optimized PI controller. The accomplishment of the system 1 in terms of ST, OS, and US of area frequency and tie-line power deviation is mentioned in Table 2b.…”

Section: Test System-i (Two-area Nonreheat System Without Nonlinearmentioning

confidence: 96%

“…An interconnected nonreheat two-area thermal power system (test system 1) widely used in literature is investigated. 18,33,37 In Figure 1, the system model is represented. Each area rating is 2000 MW, and 1000 MW is considered as nominal load.…”

Section: Mathematical Modeling Of the Systemmentioning

confidence: 99%

See 2 more Smart Citations

Performance analysis of dual‐mode PI controller using quasi‐oppositional whale optimization algorithm for load frequency control

Simhadri

Mohanty

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In this work, a novel optimization technique named quasi‐oppositional whale optimization algorithm (QOWOA) is employed for the LFC problem. In this study to improve the convergence speed, accuracy, and reliability of WOA, WOA is integrated with quasi‐oppositional–based learning theory and applied first time for the LFC problem. To show the efficacy of the QOWOA technique, at first, it is applied to an interconnected two‐area system with governor dead band nonlinearity then expanded to a realistic three‐area hybrid power system, and another system with two‐area four‐unit hydro‐thermal system is considered. Proportional‐integral and dual‐mode PI controllers are considered as secondary controller for system analysis. The optimization supremacy of the QOWOA technique is validated contrasting the outcomes with original WOA, moth flame optimization algorithm, other recent competitive algorithms, and fuzzy PI controller for an identical system. Robustness of the projected approach is tested with random load perturbation at area‐1 and step load perturbation at diverse location at diverse instant that establishes the efficacy of QOWOA. The critical system analysis demonstrates that the proposed QOWOA technique may be effectively applied for the LFC problem.

show abstract

Section: Results and Analysismentioning

confidence: 99%

Section: Test System-i (Two-area Nonreheat System Without Nonlinearmentioning

confidence: 96%

See 1 more Smart Citation

Performance analysis of dual‐mode PI controller using quasi‐oppositional whale optimization algorithm for load frequency control

Simhadri

Mohanty

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…In OBL, an opposite atom (population) is generated simultaneously for each atom (population) in the current population to provide better candidate solution. In the literature (Guha et al 2017), it has been proved that creating an opposite candidate solution enhances the probability of producing better candidate solution and getting closer to the global optimum solution when compared to creating a random candidate solution. This opposite candidate solution is created by taking the mirror position of the current population (Guha et al 2017).…”

Section: Quasi-opposition: a Conceptmentioning

confidence: 99%

Renewable Energy-Based Load Frequency Stabilization of Interconnected Power Systems Using Quasi-Oppositional Dragonfly Algorithm

Vedik¹,

Kumar²,

Deshmukh³

et al. 2020

J Control Autom Electr Syst

View full text Add to dashboard Cite

It is already established that the renewable integration effects to the power system are nonzero and become more important with large penetrations. Thereby, the impacts of renewable energy sources (RESs) after integration are studied in this work to stabilize grid frequency of the studied test power system model. Initially, the two-area power system model is studied as the test system. The purpose is to show the tuning efficiency of non-conventional quasi-oppositional dragonfly algorithm (QODA) algorithm as compared to conventional way of tuning technique. It is showed that QODA algorithm is quite effective to find the optimal parameters of proportional-integral-derivative (PID) controller in load frequency control performance. Further, the three-area power system model integrated with RESs is studied. The work done here is to study the impacts of wind turbine generation, solar thermal power generation and solar photovoltaic on system frequency oscillations. The PID controller is employed as the supplementary control task, and its parameters are tuned by QODA algorithm. The integral of time absolute error is chosen as the objective function, and further performance indices are determined at the end of the execution of the program to examine the performance of the designed QODA-based PID controller. Following the integration of RESs, the impacts on frequency deviation through simulation results are also presented. The simulation results showed that the RESs are quite effective in regulating the power system frequency deviation understudied.

show abstract

“…Performance of the cascade PD‐PID controller has not been studied for hybrid AGC system and hence required to be investigated. Many optimization techniques such as genetic algorithm (GA), BFOA, craziness‐based particle swarm optimization (CPSO), differential evolution (DE), TLBO, SOS, KHA, GWO, BSA, and hFA‐PS have been successfully implemented for AGC system. Every algorithm has its own advantages and disadvantages such as disadvantage of GA is fine tuning of all the parameters like mutation rate, elitism percentage, crossover parameters, and fitness; BFOA is chemotaxis, reproduction, and elimination‐dispersal, which requires more simulation time compared to other algorithm and PSO is c1 and c2 have to be chosen properly to obtain a better solution, and so on.…”

Section: Introductionmentioning

confidence: 99%

Analysis of moth flame optimization optimized cascade proportional‐integral‐proportional‐derivative controller with filter for automatic generation control system incorporating solar thermal power plant

Acharyulu¹,

Mohanty

Hota

2020

Optim Control Appl Methods

View full text Add to dashboard Cite

In the fast developing electric power system network, ancillary services like automatic generation control (AGC) plays a vital and significant role to ensure good quality of power supply in the system. To distribute good quality of power, a hybrid AGC system along with an efficient and intelligent controller is compelled. So, in this article, a cascaded proportional-integral (PI)-proportional-derivative (PD) controller with filter (PI-PDF) is proposed as secondary controller for AGC system. A nature inspired optimization algorithm named as moth flame optimization (MFO) algorithm is employed for simultaneous optimization of controller gains. Initially, a two-area interconnected nonreheat thermal power system is investigated. Analysis revealed that MFO-tuned PI controller performs better than the different optimization techniques tuned PI controller for the same system and PI-PDF controller performs better than PI controller does. Then, the study is extended to a three-area interconnected hybrid system with proper generation rate constraint. Area-1 consists of solar thermal-thermal unit; area-2 consists of thermal-hydro unit and area-3 thermal-gas unit as generating sources. Performance of PI-PDF controller is compared with classical controllers such as PI, PID, PIDF, and PI-PD controller without filter. Result analysis divulges that MFO-tuned PI-PDF controller performs better than all other controllers considered in this article. Robustness of the PI-PDF controller is evaluated using parameter variations and random load variation.

show abstract

Quasi-oppositional symbiotic organism search algorithm applied to load frequency control

Cited by 86 publications

References 42 publications

Performance analysis of dual‐mode PI controller using quasi‐oppositional whale optimization algorithm for load frequency control

Performance analysis of dual‐mode PI controller using quasi‐oppositional whale optimization algorithm for load frequency control

Renewable Energy-Based Load Frequency Stabilization of Interconnected Power Systems Using Quasi-Oppositional Dragonfly Algorithm

Analysis of moth flame optimization optimized cascade proportional‐integral‐proportional‐derivative controller with filter for automatic generation control system incorporating solar thermal power plant

Contact Info

Product

Resources

About