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Application of WOA Tuned Type-2 FLC for LFC of Two Area Power System With RFB and Solar Park Considering TCPS in Interline

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IEEE ACCESS 2022年 10卷 112007-112018页

作者： Soliman, Ahmed Mohammed Attiya Eldin, Mostafa Bahaa Mehanna, Mohammed Ahmed Al Azhar Univ Fac Engn Dept Elect Engn Cairo 1427 Egypt Elswedy Elect Ind Mech Elect & Plumbing MEP Tech Off New Cairo 11853 Egypt

In this article, interval type-2 fuzzy logic controller (IT2FLC) has been proposed as secondary load frequency controller (LFC) for hybrid two area power system instead of type-1 fuzzy logic controller (T1FLC) and PID based controllers. The investigated power system integrates both conventional and renewable power resources like large scale solar parks. These large scale solar parks in addition to demand load changes increases load frequency control problems due to their continuous and severe output power variation. In order to reduce the effect of solar irradiance variation on the power system, another type-2 fuzzy logic controller has been proposed to control the output for the solar park during cloudy days instead of maximum power point trackers. As one of the best energy storage systems utilized in modern power systems, Reduction oxidation flow battery (RFB) has been integrated in the investigated power system to act as fast active power source which absorbs and discharges power during disturbances caused by generation or demand changes. Power flow controller like thyristor controlled phase shifter (TCPS) has been proposed in this paper to control tie-line power shared between generating areas during system disturbances. In order to enhance the dynamic performance for the proposed controller, meta-heuristic nature inspired optimization algorithm like whale optimization algorithm (WOA) has been proposed for proposed controller gains off line tuning. The superiority of the proposed IT2FLC controller against T1FLC controller has been investigated by simulating their performance during severe demand load changes and solar irradiance variations, while WOA enhanced the dynamic performance of the proposed controller compared to other optimization algorithms like particle swarm optimization (PSO) and grey wolf optimization (GWO).

关键词： Power system stability Power generation whale optimization algorithms Uncertainty Renewable energy sources Linguistics Heuristic algorithms Flexible AC transmission systems Solar energy Fuzzy logic Area control error (ACE) automatic generation control (AGC) flexible alternating current transmission system (FACTS) interval type-2 fuzzy logic system (IT2FLC) load frequency control (LFC) redox flow battery (RFB) solar park thyristor controlled phase shifter (TCPS) whale optimization algorithm (WOA)

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A Novel Renewable Power Generation Prediction Through Enhanced Artificial Orcas Assisted Ensemble Dilated Deep Learning Network

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IEEE ACCESS 2024年 12卷 44207-44223页

作者： Che, Zhifeng Amirthasaravanan, A. Al-Razgan, Muna Awwad, Emad Mahrous Mohamed, Mohamed Yasin Noor Tyagi, Vaibhav Bhushan Xinxiang Vocat & Tech Coll Xinxiang 453000 Henan Peoples R China SRM Inst Sci & Technol Dept Comp Technol Kattankulathur 603203 Tamil Nadu India King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11345 Saudi Arabia King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Sultan Qaboos Univ Dept Math & Informat Technol Muscat 123 Oman SIMATS Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 600077 Tamil Nadu India ISBAT Univ Fac Engn Kampala Uganda

The different energy resource generation tends to have high-level variation, making the power supply complex for the end-users. Because of the intermittent nature, the variations occur by time, weather conditions, and output energy. Hence, this research aims to develop a new "Renewable Power Generation Prediction (RPGP)" model using Deep Learning (DL) to give the end user a reliable power supply. The data aggregation process initially accumulated the data in a normalized and structured format. Then, the data cleaning and scaling are performed to decrease the outliers and varying ranges of values. A higher-order statistical feature was attained from the cleaned and scaled data. This statistical feature was given to "Optimal Weight Computation Ensemble Dilated Deep Network (OWC-EDDNet)" to predict generated power. In this EDDLNet, networks such as "Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN)" are employed to predict the renewable generated power. Finally, the prediction score attained from all deep networks is multiplied by the optimized weight to get the final prediction outcome, where the weights are optimally determined with the support of the Enhanced Artificial Orcas Algorithm (EAOA). The extensive empirical results were analyzed among traditional algorithms and prediction models to showcase the efficacy of the designed energy generation prediction scheme.

关键词： Renewable energy sources Predictive models whale optimization algorithms Higher order statistics Power supplies Power generation planning Long short term memory Recurrent neural networks Meteorology Energy resources Energy management Weight measurement Data aggregation Artificial neural networks Renewable power generation prediction enhanced artificial orcas algorithm higher order statistical features optimal weight computation ensemble dilated deep network

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Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

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IEEE ACCESS 2023年 11卷 129507-129535页

作者： Zhang, Shengcai Fu, Qiming An, Dezhi Gansu Univ Polit Sci & Law Sch Cyber Secur Lanzhou 730070 Peoples R China

The widespread adoption of Internet-of-Things (IoT) devices has resulted in a comprehensive transformation of human life. However, the network security challenges posed by the IoT devices have become increasingly severe, necessitating the implementation of effective security mechanisms. Network security situational awareness enables an effective network state prediction for better formulation of network security defense strategies. Existing network security situational prediction methods are typically constrained by situational sequence data, especially those sequences with a high degree of non-stationarity, leading to unstable predictions and low performance. Moreover, in real-world application scenarios, the network security situational sequences are often highly non-stationary. To address these challenges, we introduce a novel hybrid prediction model named Variational Mode Decomposition (VMD) - Dynamic whale optimization Algorithm (DWOA) - Bidirectional Gated Recurrent Unit (BiGRU) - Attention Mechanism (ATTN). The proposed model integrates VMD, BiGRU, ATTN, and DWOA. Initially, network security situational awareness sequences are processed using VMD to decompose them into a series of subsequences, thus reducing the non-stationarity of the original sequences. Subsequently, an enhanced DWOA optimization algorithm is introduced for tuning the hyperparameters of the BiGRU-ATTN network. Ultimately, BiGRU-ATTN is employed to predict each of these subsequences, which are then aggregated to yield the final network security situational prediction value. When compared with several existing methods on public network security datasets, the proposed VMD-DWOA-BiGRU-ATTN method demonstrated an improvement in the R<^>2 values ranging from 6.34% to 52.61%. These results substantiate that the model significantly enhances predictive performance.

关键词： Network security Predictive models Security Time series analysis Data models Prediction algorithms Internet of Things Bidirectional control whale optimization algorithms Attention mechanism bi-directional gated recurrent unit dynamic whale optimization algorithm network security situation prediction variational mode decomposition

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Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model

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IEEE ACCESS 2024年 12卷 58750-58777页

作者： Joseph, Lionel P. Deo, Ravinesh C. Casillas-Perez, David Prasad, Ramendra Raj, Nawin Salcedo-Sanz, Sancho Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Ctr Appl Climate Sci Toowoomba Qld 4350 Australia Univ Rey Juan Carlos Dept Signal Proc & Commun Madrid 28942 Spain Univ Fiji Sch Sci & Technol Dept Sci Lautoka Fiji Univ Alcala Dept Signal Proc & Commun Madrid 28805 Spain

Wind, being a clean and sustainable resource, boasts environmental advantages. However, its electricity generation faces challenges due to unpredictable variations in wind speed (WS). Accurate predictions of these variations would allow mixed grids to adjust their energy mix in real-time, ensuring overall stability. For this purpose, the paper develops a new hybrid gated additive tree ensemble (H-GATE) model for accurate multi-step-ahead WS predictions. First, the multivariate empirical mode decomposition (MEMD) simultaneously demarcates the multivariate data into intrinsic mode functions (IMFs) and residuals. These components represent underlying trends, periodicity, and stochastic patterns in WS variations. The IMF and residual components are pooled in respective sets, and an opposition-based whale optimization algorithm (OBWOA) is applied for dimensionality reduction. The selected features are used by GATE tuned with Bayesian optimization (BO) to forecast the individual IMF and residual components. The outputs are summed to obtain the final multi-step-ahead WS forecasts. The proposed H-GATE is benchmarked against standalone (S-GATE, S-CLSTM, and S-ABR) and hybrid (H-CLSTM and H-ABR) models. Based on all statistical metrics and diagnostic plots, H-GATE outperforms all comparative models at all forecast horizons, accumulating the lowest mean absolute percentage error (MAPE) of 6.13 - 9.93% (at t(L+1) ), 8.67 - 14.07% (at t(L+2 )), and 11.60 - 18.37% (at t(L+3) ) across all three sites. This novel multi-step-ahead WS forecasting strategy can significantly benefit grid operators by helping anticipate fluctuations in wind power generation. This can assist in optimizing energy dispatch schedules, reducing reliance on backup power sources, and enhancing overall grid stability. Practical implementation of this method can help meet the rising energy demands through renewable wind energy.

关键词： Forecasting Long short term memory Predictive models Logic gates Computational modeling Autoregressive processes Wind power generation Velocity measurement Wind speed whale optimization algorithms Bayes methods Wind speed forecasting gated additive tree ensemble multivariate empirical mode decomposition opposition-based whale optimization algorithm Bayesian optimization

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Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier

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IEEE ACCESS 2024年 12卷 47155-47172页

作者： Mishra, Sairam Mallick, Ranjan Kumar Gadanayak, Debadatta Amaresh Nayak, Pravati Sharma, Renu Panda, Gayadhar Al-Numay, Mohammed S. Siano, Pierluigi Siksha O Anushandhan Deemed Univ Dept Elect Engn Bhubaneswar 751030 Odisha India Siksha O Anusandhan Deemed Univ Dept Elect & Elect Engn Bhubaneswar 751030 Odisha India Natl Inst Technol Meghalaya Shillong 793003 Meghalaya India King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Univ Salerno Dept Management & Innovat Syst I-84084 Salerno Italy

The modern era power system is constantly undergoing constructive changes and implementations both in source and load side. Certainly, the distributed generators, unconventional/nonlinear loads, charging stations etc are mostly integrated through power electronics interfaces. As a result, frequent power quality disturbances appear in the system that is to be mitigated at the earliest. Since detection is the prerequisite for mitigation, therefore the article presents a novel intelligent power quality detection scheme to detect and classify the PQ Events. At first, the energy feature of the 5 band limited modes are calculated from variational mode decomposed voltage signals. Then the mode energy features are utilized to train a novel Hybrid Arithmetic whale Optimized light gradient boosting machine classifier. A total of 15 different PQ events have been investigated and exceptional classification results have obtained with optimum computational complexity, both under noiseless and noisy conditions. Moreover, the accuracy of the proposed PQ classification schemes found to be towering against other related pre-published works. Finally, the ability of the proposed detection scheme is validated in real time though OPAL-RT 4510 and grid simulator hardware in loop setup.

关键词： Transient analysis Boosting Power quality Mathematical models Harmonic analysis Bagging whale optimization algorithms optimization methods Gradient methods variational mode decomposition whale optimization arithmetic optimization light gradient boosting machine

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The Power Load Forecasting Model of Combined SaDE-ELM and FA-CAWOA-SVM Based on CSSA

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IEEE ACCESS 2024年 12卷 41870-41882页

作者： Wang, Zuoxun Ku, Yangyang Liu, Jian Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Informat & Automat Jinan 250353 Peoples R China

The security and stability of the power grid are directly affected by the accuracy of power load forecasting. Additionally, it plays an important role in power system planning. In order to enhance forecasting accuracy, a combined forecasting model is proposed in this paper. Firstly, preprocessing of the original data is conducted through improved singular spectrum analysis. Subsequently, load data prediction is carried out by the adaptive evolutionary extreme learning machine (SaDE-ELM). Additionally, load data prediction is performed using the support vector machine model(SVM), which is optimized by the chaotic adaptive whale algorithm based on the firefly disturbance strategy (FA-CAWOA-LSSVM). In the final step, the weight coefficients of the two prediction models are calculated by the chaotic sparrow search algorithm (CSSA). The load prediction results are obtained through the weighted summation of the two predictions. Superior performance is demonstrated by the combined prediction model compared with other single prediction models. The data preprocessing method, based on improved singular spectrum analysis, effectively enhances prediction accuracy.

关键词： Spectral analysis Load management Load forecasting Predictive models Adaptation models Forecasting Support vector machines Stability analysis Security whale optimization algorithms Singular spectrum analysis combinatorial forecasting model power load forecasting

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Improved Marine Predators Algorithm Optimized BiGRU for Strip Exit Thickness Prediction

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IEEE ACCESS 2024年 12卷 56719-56729页

作者： Luo, Hao Chen, Jiaxuan Sun, Zhan Zhang, Ying Zhang, Li Liaoning Univ Coll Informat Shenyang 110036 Peoples R China

The strip exit thickness is an important indicator for measuring the production quality of steel rolling products. Controlling its accuracy is crucial in producing high-quality rolling products in the steel industry. This study proposed an improved marine predators algorithm (IMPA) optimized bidirectional gated recurrent unit (BiGRU) for predicting the strip exit thickness. Firstly, an adaptive weight factor was introduced into the marine predators algorithm (MPA) to overcome the drawbacks of slow early convergence speed and limited search range of the algorithm, addressing the issue of local optimization. Secondly, an average fitness strategy was developed to enhance the quality of the algorithm population, improve optimization accuracy, and strengthen algorithm stability. In addition, IMPA was used to optimize parameters, reducing the impact of hyper-parameters such as the number of hidden layer neurons, learning rate, and batch size on the prediction accuracy of BiGRU, thereby establishing IMPA-BiGRU model. The experiments were conducted on an actual rolling process dataset. The experimental results showed that IMPA outperformed traditional MPA, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), and recent variants of MPA. The IMPA-BiGRU also demonstrated excellent prediction capabilities for strip exit thickness, meeting the practical production requirements for high-quality rolling products.

关键词： Prediction algorithms Statistics Social factors Predictive models optimization Vectors Parameter estimation Feature detection Marine animals Heuristic algorithms whale optimization algorithms BiGRU parameter optimization feature selection marine predators algorithm heuristic algorithm strip thickness

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Wild Horse Optimizer-Based Spiral Updating for Feature Selection

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IEEE ACCESS 2022年 10卷 106258-106274页

作者： Ewees, Ahmed A. Ismail, Fatma H. Ghoniem, Rania M. Univ Bisha Coll Comp & Informat Technol Dept Informat Syst Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat 34517 Egypt Misr Int Univ Fac Comp Sci Cairo 11341 Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia

Feature selection (FS) is a vital and challenging process in several domains, including data mining, data clustering, text mining, education, biology, medicine, public health, machine learning, image processing, and so on. The greedy and comprehensive algorithm methods cannot identify the best subset amid the rising number of features. Thus, swarm-based algorithms are becoming more popular for identifying the best group of features. This study relies on the spiral-updated position of the whale optimization Algorithm (WOA) to propose an improved version of the Wild Horse Optimizer (WHO). This improvement enhances the WHO's ability to update solutions and explore various possibilities in the search domain. The proposed method (WHOW) was assessed using two experiments to confirm the efficacy of the improved optimizer. The first experiment was global optimization using CEC 2019 benchmark functions, whereas the second was an FS by testing 20 benchmark datasets. The results obtained using the proposed WHOW method were compared to some popular algorithms over the benchmark datasets of global optimization and FS. The experimental results reflect the superiority of WHOW in the solutions to different optimization problems and its ability to select prominent features over most benchmark datasets. These results are due to the implementation of bubble nets in the WHO using spiral movement, which promotes flexibility and performance.

关键词： Convergence Behavioral sciences Statistics Social factors Benchmark testing Metaheuristics whale optimization algorithms Feature extraction Wild horse optimizer whale optimization algorithm spiral updating position global optimization feature selection

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An Exact Zoeppritz Based Prestack Inversion Using whale optimization Particle Filter Algorithm Under Bayesian Framework

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷

作者： Tang, Jing Li, Peng Huang, Xuri Lai, Qiang Wang, Ding Southwest Petr Univ Sch Geosci & Technol Chengdu Peoples R China Southwest Petr Univ Sch Geosci & Technol State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu Peoples R China PetroChina Southwest Oil & Gasfield Co Chengdu Peoples R China Shaoxing Univ Key Lab Rock Mech & Geohazards Zhejiang Prov Shaoxing Peoples R China

Conventional amplitude versus offset (AVO) inversion methods are mainly based on various Zoeppritz approximations. The assumptions of small contrast and linear relationship lead to the most inversion methods being difficult to have high inversion accuracy. In this article, the exact Zoeppritz equation is used to establish the prestack inversion method under the Bayesian framework. It integrates multisource information to generate posterior distributions of P-, S-wave velocity and density. In the Bayesian theory, the prior model works as the regularization term which has a strong effect on the inversion results. The strategy to obtain a relatively accurate prior model can improve the inversion accuracy. Therefore, an exact Zoeppritz equation based nonlinear AVO inversion algorithm combing whale optimization particle filtering (WOPF) is proposed. The WOPF method can generate a relatively stable and accurate initial model for the Bayesian prestack inversion. We validate the new method through two synthetic models and field data. Comparisons are made with the conventional linear and nonlinear AVO inversion methods. The results show that the proposed method can provide much more accurate inverted elastic parameters in different geological conditions.

关键词： Mathematical models whale optimization algorithms Bayes methods whales Particle filters Fluids Filtering Amplitude versus offset (AVO) exact Zoeppritz equation particle filter (PF) whale optimization particle filter (WOPF) algorithm

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Prediction of Converter Tapping Weight Based on Principal Component Analysis-whale optimization Algorithm-Backpropagation Algorithm Neural Network Model

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STEEL RESEARCH INTERNATIONAL 2024年第3期95卷

作者： Xue, Chao Xu, Weili Bao, Yanping Zhao, Lihua Liu, Xin Zheng, Ruixuan Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 100083 Peoples R China Jianlong Xilin Iron & Steel Co Ltd Technol Ctr Yichun 153000 Peoples R China Univ Sci & Technol Beijing Sch Met & Ecol Engn Beijing 100083 Peoples R China

Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis-whale optimization algorithm-backpropagation algorithm (PCA-WOA-BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted;the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately. By using the collected molten iron and scrap information of the converter through the adjusted principal component analysis-whale optimization algorithm-backpropagation algorithm tapping weight prediction model, a very accurate prediction value of the converter tapping weight can be obtained in the early stage of converter *** (c) 2024 WILEY-VCH GmbH

关键词： backpropagation algorithm neural networks data analyses industrial applications tapping weights whale optimization algorithms

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