Modified multiverse optimizer technique-based two degree of freedom fuzzy PID controller for frequency control of microgrid systems with hydrogen aqua electrolyzer fuel cell unit

Mishra, Siba Prasad; Nayak, Pratap Chandra; Prusty, Ramesh Chandra; Panda, Sukanta

doi:10.1007/s00521-022-07453-5

Cited by 23 publications

(9 citation statements)

References 18 publications

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“…It's conceptual model of MVO algorithm is shown in Figure 1. The algorithm defines the candidate solution as an initializing universe [38]. Assuming that the number of initializing population was NP and the vector dimension to be solved was NQ, the initial universe U could be expressed as…”

Section: Multiverse Optimizer Algorithmmentioning

confidence: 99%

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

et al. 2022

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Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

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Section: Multiverse Optimizer Algorithmmentioning

confidence: 99%

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

et al. 2022

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“…New sophisticated methods are employed to adjust the controller coefficients to overcome the complexity of the control methods. For example, the researchers used the self-tuned algorithm (STA) [37], marine predator optimization algorithm (MPA) [38], chaos game optimization (CGO) [39], modified multiverse optimizer [40], improved based fitness-dependent optimizer [41], equilibrium optimizer hybridized with slime mould optimization algorithm [42], artificial ecosystem optimization (AEO) [43], sunflower optimization [44], butterfly optimization algorithm (BOA) [45], smell agent optimization (SAO) [46], pathfinder optimizer algorithm (PFA) [47], mine blast algorithm (MBA) [48], Fox optimizer algorithm (FOA) [49], and socialspider optimizer [50]. According to research [51], the TID with filter (TIDF) configured with DE outperformed the I/ PI/PID controller.…”

Section: Introductionmentioning

confidence: 99%

Cascaded Fractional‐Order Controller‐Based Load Frequency Regulation for Diverse Multigeneration Sources Incorporated with Nuclear Power Plant

Ye,

Daraz,

Basit

et al. 2024

International Journal of Energy Research

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To sustain a system frequency within acceptable limits, it is widely conceded that retaining a power balance between generation and demand is necessary. In order to regulate the frequency of power systems (PSs) this article proposes a novel cascaded based fractional-order controller termed as fractional-order integer- (FOI-) fractional-order proportional integral with double derivative (FOPIDD2). In addition to redox flow batteries and capacitive energy storage, the recommended control strategy has been validated with gas, thermal reheat, hydro, and nuclear power systems. Additionally, a newly designed algorithm known as squid game optimizer (SGO) optimizes the gains of the new FOI-FOPIDD2 controller. The squid game optimizer technique is inspired by the fundamental principles of a conventional Korean sport. It employs a population of candidate solutions and iteratively adjusts the control parameters to discover the optimal set that reduces frequency abnormalities and improves system stability. A comparison is also made between the controller’s performance and benchmarks, including the jellyfish search algorithm, the firefly algorithm, the grey wolf optimizer, and the particle swarm algorithm. The proposed algorithms reduced peak overshoot as compared to grey wolf optimizer algorithm by 35.34%, 46.78%, and 76.89%; jellyfish search optimization algorithm by 34.76%, 77.22%, and 82.56%; and firefly algorithm by 82.67%, 89.23%, and 29.67% for frequency variations in area 1, area 2 and tie line power, respectively. Furthermore, SGO-FOI-FOPIDD2 controllers under different loading circumstances and conditions were evaluated and endorsed for their ability to withstand uncertainties in power system parameters.

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“…Sophisticated new techniques are implemented in order to optimize the controller parameters and surmount the intricacy of the control schemes. As an illustration, the authors implemented the following algorithms: self-tuned algorithm (STA) 35 , Bull–Lion Optimization (BLO) 36 , Differential Evolution based PI regulator for automatic generation control 37 ,chaos game optimization (CGO) 38 , Krill herd algorithm for AGC of multi-region non-linear power system 39 , improved fitness dependent algorithm based tuned modified FOPID controller employed in deregulated environment 40 , modified multiverse optimizer 41 , and Sunflower optimization algorithm (BOA) 42 . The authors in 43 have been using pathfinder optimizer algorithm (PFA) to balance load power demand employing FOTID regulator.…”

Section: Introductionmentioning

confidence: 99%

Load frequency stabilization of distinct hybrid conventional and renewable power systems incorporated with electrical vehicles and capacitive energy storage

Daraz,

Alrajhi,

Basit

et al. 2024

Sci Rep

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Maintaining a power balance between generation and demand is generally acknowledged as being essential to maintaining a system frequency within reasonable bounds. This is especially important for linked renewable-based hybrid power systems (HPS), where disruptions are more likely to occur. This paper suggests a prominent modified “Fractional order-proportional-integral with double derivative (FOPIDD2) controller” as an innovative HPS controller in order to navigate these obstacles. The recommended control approach has been validated in power systems including wind, reheat thermal, solar, and hydro generating, as well as capacitive energy storage and electric vehicle. The improved controller’s performance is evaluated by comparing it to regular FOPID, PID, and PIDD2 controllers. Furthermore, the gains of the newly structured FOPIDD2 controller are optimized using a newly intended algorithm terms as squid game optimizer (SGO). The controller’s performance is compared to benchmarks such as the grey wolf optimizer (GWO) and jellyfish search optimization. By comparing performance characteristics such as maximum frequency undershoot/overshoot, and steadying time, the SGO-FOPIDD2 controller outperforms the other techniques. The suggested SGO optimized FOPIDD2 controller was analyzed and validated for its ability to withstand the influence of power system parameter uncertainties under various loading scenarios and situations. Without any complicated design, the results show that the new controller can work steadily and regulate frequency with an appropriate controller coefficient.

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Modified multiverse optimizer technique-based two degree of freedom fuzzy PID controller for frequency control of microgrid systems with hydrogen aqua electrolyzer fuel cell unit

Cited by 23 publications

References 18 publications

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Cascaded Fractional‐Order Controller‐Based Load Frequency Regulation for Diverse Multigeneration Sources Incorporated with Nuclear Power Plant

Load frequency stabilization of distinct hybrid conventional and renewable power systems incorporated with electrical vehicles and capacitive energy storage

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