Frequency Stability Enhancement Using Differential-Evolution- and Genetic-Algorithm-Optimized Intelligent Controllers in Multiple Virtual Synchronous Machine Systems

Feleke, Solomon; Pydi, Balamurali; Satish, Raavi; Kotb, Hossam; Alenezi, Mohammed; Shouran, Mokhtar

doi:10.3390/su151813892

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…The first stage is a multi-objective optimization problem solved using the improved multi-objective gray wolf algorithm [28]. The second stage is solved using a differential evolutionary algorithm [29], in which the improved multi-objective gray wolf algorithm solves the first stage to obtain multiple Pareto optimal solutions. In this paper, a fuzzy affiliation function is developed to evaluate the objective function, and the optimal compromise solution is selected [30].…”

Section: Case Study 61 Descriptionmentioning

confidence: 99%

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Wang,

Du,

Wang

et al. 2024

Energies

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Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.

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Section: Case Study 61 Descriptionmentioning

confidence: 99%

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Wang,

Du,

Wang

et al. 2024

Energies

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“…The inputs to the corrected values of the three parameters of the PID and the system deviation into the PID controller, and obtains the corrected values o the three parameters 𝐾 𝑝 , 𝐾 𝑖 , and 𝐾 𝑑 . In the iterative process, the fitness function is needed to calculate the fitness value o each individual, and then evaluate the advantages and disadvantages of the individual The Integral of the Time-weighted Absolute Error (ITAE) metric, which has the ad vantages of fast, smooth, and low overshooting, has been adopted by most of the litera tures [27][28][29], therefore, this paper introduces it into the performance evaluation of the precision fertilizer control system, which serves as an important reference index of the controller and as the fitness function of the BA optimization algorithm. The ITAE criterion In the iterative process, the fitness function is needed to calculate the fitness value of each individual, and then evaluate the advantages and disadvantages of the individual.…”

Section: Design Of Bat-optimized Variable-domain Fuzzy Pid Controllermentioning

confidence: 99%

“…The ITAE criterion In the iterative process, the fitness function is needed to calculate the fitness value of each individual, and then evaluate the advantages and disadvantages of the individual. The Integral of the Time-weighted Absolute Error (ITAE) metric, which has the advantages of fast, smooth, and low overshooting, has been adopted by most of the literatures [27][28][29], therefore, this paper introduces it into the performance evaluation of the precision fertilizer control system, which serves as an important reference index of the controller and as the fitness function of the BA optimization algorithm. The ITAE criterion, i.e., the Time-Multiplied Absolute Error Integral Minimization criterion, can be expressed as follows:…”

Section: Design Of Bat-optimized Variable-domain Fuzzy Pid Controllermentioning

confidence: 99%

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

Guo,

Zhu,

Zhao

et al. 2024

Processes

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Due to the time-varying, hysteresis and nonlinear characteristics of fertilizer concentration control in the water–fertilizer ratio control system, common control algorithms such as PID and fuzzy PID cannot obtain the expected control effect. In order to accurately control the cotton field water–fertilizer ratio regulation system drip irrigation process of the water–fertilizer ratio that will be controlled within a reasonable range, it is needed to design a bat-optimized variable-domain fuzzy PID water–fertilizer ratio control strategy, through the use of bat algorithm to find out the optimal expansion factor and the best domain of the current conditions, and then according to the changes in working conditions to automatically adjust the fuzzy control of the domain, through the control of the valve openings to change the fertilizer pump back to the amount of water. Realize the fast and precise control of fertilizer concentration in the water–fertilizer ratio control system. Comparative tests were conducted to verify the traditional PID, fuzzy PID, variable domain fuzzy PID and bat-optimized variable-domain fuzzy PID control algorithms. The results show that: if the water–fertilizer ratio is adjusted to 50:1 from the startup, the adjustment time required to reach the target water–fertilizer ratio under the bat-optimized variable-domain fuzzy PID control is 15.29 s, and the maximum overshooting amount is 16.28%, which is a smaller adjustment time and overshooting amount; if the water–fertilizer ratio is adjusted to 40:1 from 50:1, the advantages of bat-optimized variable-domain fuzzy PID are more obvious, with the best balance of response speed, overshooting amount and optimal control effect. In terms of response speed, overshooting amount and regulation time, the optimal balance is achieved, showing the optimal control effect. It is proved that the performance of the water–fertilizer ratio regulation system in cotton field under bat-optimized variable-domain fuzzy PID control designed in this paper can meet the actual production requirements, and these findings can help to develop precise irrigation technology for cotton cultivation under drip irrigation conditions.

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“…This is widely used in SER to reduce processing time and enhance recognition efficiency. Differential evolution (DE) mimics the natural concept of survival of the fittest, and gradually converges towards an optimal or near-optimal solution [5][6][7]. DE is known for its computational efficiency in optimizing feature subsets.…”

Section: Introductionmentioning

confidence: 99%

English Speech Emotion Classification Based on Multi-Objective Differential Evolution

Yue,

Hu,

Chu

et al. 2023

Applied Sciences

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Speech signals involve speakers’ emotional states and language information, which is very important for human–computer interaction that recognizes speakers’ emotions. Feature selection is a common method for improving recognition accuracy. In this paper, we propose a multi-objective optimization method based on differential evolution (MODE-NSF) that maximizes recognition accuracy and minimizes the number of selected features (NSF). First, the Mel-frequency cepstral coefficient (MFCC) features and pitch features are extracted from speech signals. Then, the proposed algorithm implements feature selection where the NSF guides the initialization, crossover, and mutation of the algorithm. We used four English speech emotion datasets, and K-nearest neighbor (KNN) and random forest (RF) classifiers to validate the performance of the proposed algorithm. The results illustrate that MODE-NSF is superior to other multi-objective algorithms in terms of the hypervolume (HV), inverted generational distance (IGD), Pareto optimal solutions, and running time. MODE-NSF achieved an accuracy of 49% using eNTERFACE05, 53% using the Ryerson audio-visual database of emotional speech and song (RAVDESS), 76% using Surrey audio-visual expressed emotion (SAVEE) database, and 98% using the Toronto emotional speech set (TESS). MODE-NSF obtained good recognition results, which provides a basis for the establishment of emotional models.

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Frequency Stability Enhancement Using Differential-Evolution- and Genetic-Algorithm-Optimized Intelligent Controllers in Multiple Virtual Synchronous Machine Systems

Cited by 5 publications

References 37 publications

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

English Speech Emotion Classification Based on Multi-Objective Differential Evolution

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