Pub Date : 2023-04-28DOI: 10.2174/2352096516666230428103051
Anisha Asmy N.R., R. J
Microgrids conquer a significant role in the evolution of distributed and modern grids from the traditional electricity system. However, microgrids with renewable energy sources connected to them often incur grid instability issues, due to the intermittent nature of these sources. This work aims to study Microgrids with Electric vehicles as a backup energy source and maintain the system’s frequency that can overcome this issue. This paper uses an autonomous control algorithm in an islanded ac microgrid to regulate the active power depending on the irradiation and load scenarios, thereby maintaining the system frequency and stability. The controller also keeps track of the battery's charge level, keeping it from overcharging or over-discharging conditions. The PI (Proportional Integral) and Fractional Order Proportional Integral (FOPI) controllers were compared, with the best controller utilized for system simulations. Simulations are presented with MATLAB/Simulink for an Islanded Photo Voltaic AC microgrid system with the electric vehicle's battery connected to it as a source of backup energy. The system's effect is exhibited under varied irradiations and load levels, and the findings demonstrate the control algorithm's adaptability. This work attempts to discover the capability of the control technique to maintaining the stability of an AC islanded microgrid system under diverse irradiation and load situations, thereby maintaining the system's frequency and the State of Charge (SoC) of the battery of an electric vehicle under specified levels.
微电网在从传统电力系统向分布式和现代电网发展的过程中发挥着重要作用。然而，由于可再生能源的间歇性，与之相连的微电网往往会引发电网不稳定问题。本工作旨在研究以电动汽车作为备用能源的微电网，并保持系统的频率，以克服这一问题。本文在孤岛交流微电网中采用自主控制算法，根据辐照和负荷情况调节有功功率，从而保持系统频率和稳定性。控制器还可以跟踪电池的充电水平，防止电池过度充电或过度放电。比较了PI (Proportional Integral)和分数阶比例积分(Fractional Order Proportional Integral, FOPI)两种控制器，选择了最佳控制器进行系统仿真。利用MATLAB/Simulink对孤岛式光伏交流微电网系统进行了仿真，并将电动汽车电池作为备用电源接入该系统。实验结果表明，该控制算法具有较强的适应性。这项工作试图发现控制技术在不同辐射和负载情况下维持交流孤岛微电网系统稳定性的能力，从而维持系统的频率和电动汽车电池在规定水平下的充电状态(SoC)。
{"title":"Fractional Order Controller For Power Control In AC Islanded PV Microgrid Using Electric Vehicles","authors":"Anisha Asmy N.R., R. J","doi":"10.2174/2352096516666230428103051","DOIUrl":"https://doi.org/10.2174/2352096516666230428103051","url":null,"abstract":"\u0000\u0000Microgrids conquer a significant role in the evolution of distributed and modern grids from the traditional electricity system. However, microgrids with renewable energy sources connected to them often incur grid instability issues, due to the intermittent nature of these sources.\u0000\u0000\u0000\u0000This work aims to study Microgrids with Electric vehicles as a backup energy source and maintain the system’s frequency that can overcome this issue.\u0000\u0000\u0000\u0000This paper uses an autonomous control algorithm in an islanded ac microgrid to regulate the active\u0000 power depending on the irradiation and load scenarios, thereby maintaining the system frequency and stability. The controller also keeps track of the battery's charge level, keeping it from overcharging or over-discharging conditions. The PI (Proportional Integral) and Fractional Order Proportional Integral (FOPI) controllers were compared, with the best controller utilized for system simulations.\u0000\u0000\u0000\u0000Simulations are presented with MATLAB/Simulink for an Islanded Photo Voltaic AC microgrid system with the electric vehicle's battery connected to it as a source of backup energy. The system's effect is exhibited under varied irradiations and load levels, and the findings demonstrate the control algorithm's adaptability.\u0000\u0000\u0000\u0000This work attempts to discover the capability of the control technique to maintaining the stability\u0000of an AC islanded microgrid system under diverse irradiation and load situations, thereby maintaining the system's frequency and the State of Charge (SoC) of the battery of an electric vehicle under specified levels.\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"112 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87644754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-28DOI: 10.2174/2352096516666230428104141
Yuancheng Li, Jiexuan Yuan
Complex structures such as a high proportion of power electronic equipment has brought new challenges to the safe and stable operation of new-type power system, increasing the possibility of the system being attacked, especially the more complex Advanced Persistent Threat (APT). This kind of attack has a long duration and strong concealment. Traditional detection methods target a relatively single attack mode, and the time span of APT processed is relatively short. None of them can effectively capture the long-term correlation in the attack, and the detection rate is low. These methods can’t meet the safety requirements of the new-type power system. In order to solve this problem, this paper proposes an improved transformer model called STSA-transformer algorithm, and applies it to the detection of APT in new-type power systems. In the STSA-transformer model, the network traffic collected from the power system is first converted into a sequence of feature vectors, and the location information and local feature of the sequence, is extracted by combining position encoding with convolutional embedding operations, and then global characteristics of attack sequences is captured using the multi-head self-attention mechanism of the transformer encoder, the higher-frequency features of the attention are extracted through the self-learning threshold operation, combined with the PowerNorm algorithm to standardize the samples, and finally classify the network traffic of the APT. After multiple rounds of training on the model, the expected effect can be achieved and applied to the APT detection of a new-type power system. The experimental results show that the proposed STSA-transformer algorithm has better detection accuracy and lower detection false-alarm rate than traditional deep learning algorithms and machine learning algorithms.
{"title":"An APT Attack Detection Method of a New-type Power System Based on STSA-transformer","authors":"Yuancheng Li, Jiexuan Yuan","doi":"10.2174/2352096516666230428104141","DOIUrl":"https://doi.org/10.2174/2352096516666230428104141","url":null,"abstract":"\u0000\u0000Complex structures such as a high proportion of power electronic equipment has brought new challenges to the safe and stable operation of new-type power system, increasing the possibility of the system being attacked, especially the more complex Advanced Persistent Threat (APT). This kind of attack has a long duration and strong concealment.\u0000\u0000\u0000\u0000Traditional detection methods target a relatively single attack mode, and the time span of APT processed is relatively short. None of them can effectively capture the long-term correlation in the attack, and the detection rate is low. These methods can’t meet the safety requirements of the new-type power system. In order to solve this problem, this paper proposes an improved transformer model called STSA-transformer algorithm, and applies it to the detection of APT in new-type power systems.\u0000\u0000\u0000\u0000In the STSA-transformer model, the network traffic collected from the power system is first converted into a sequence of feature vectors, and the location information and local feature of the sequence, is extracted by combining position encoding with convolutional embedding operations, and then global characteristics of attack sequences is captured using the multi-head self-attention mechanism of the transformer encoder, the higher-frequency features of the attention are extracted through the self-learning threshold operation, combined with the PowerNorm algorithm to standardize the samples, and finally classify the network traffic of the APT.\u0000\u0000\u0000\u0000After multiple rounds of training on the model, the expected effect can be achieved and applied to the APT detection of a new-type power system.\u0000\u0000\u0000\u0000The experimental results show that the proposed STSA-transformer algorithm has better detection accuracy and lower detection false-alarm rate than traditional deep learning algorithms and machine learning algorithms.\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"46 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80471301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In addressing the issue of power transformer oil temperature prediction, traditional back propagation (BP) neural network algorithms have been found to suffer from local optimization and slow convergence. This study proposes an oil temperature prediction model based on an improved particle swarm optimization (PSO) neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator. The BP neural network, genetic algorithm (GA) optimization neural network, and the improved PSO neural network are compared by considering various factors, such as ambient temperature, load changes, and the number of cooler groups under different working conditions. Results show that the proposed algorithm improves the actual change trend of oil surface temperature and makes the transformer operation more stable to a certain extent. The mathematical model for predicting transformer oil temperature is clear, but the parameters in the model are uncertain and vary with time. When subjected to different operating conditions, such as ambient temperature, load changes, and the number of cooler groups acting independently or in combination, the prediction results of the oil temperature model vary with different system parameters. This paper aims to enhance the accuracy of transformer temperature prediction. In order to optimize the oil temperature prediction model, asymmetric adjustment learning factors and mutant operators are added to meet diverse system parameter requirements. The paper utilizes an oil temperature prediction model based on an improved PSO neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator to address the limitations of the standard PSO algorithm. This paper has employed a fusion algorithm of the genetic algorithm of the BP neural network and the PSO algorithm, and conducted simulation and experimental analysis. The simulation and experimental results demonstrate the accuracy and effectiveness of the fusion algorithm. This study demonstrates enhanced prediction accuracy of transformer oil surface temperature using the improved particle swarm optimization neural network algorithm. This algorithm has less prediction error under different working conditions compared to other algorithms. By increasing population diversity and combining inertia weights, the algorithm not only greatly improves its search performance but also avoids local optimization.
{"title":"Prediction of transformer oil temperature based on an improved PSO neural network algorithm","authors":"Weihan Kong, Zhiyan Zhang, Linze Li, Hongfei Zhao, Chunwen Xin","doi":"10.2174/2352096516666230427142632","DOIUrl":"https://doi.org/10.2174/2352096516666230427142632","url":null,"abstract":"\u0000\u0000In addressing the issue of power transformer oil temperature prediction, traditional back\u0000propagation (BP) neural network algorithms have been found to suffer from local optimization and\u0000slow convergence. This study proposes an oil temperature prediction model based on an improved\u0000particle swarm optimization (PSO) neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator. The BP neural network, genetic algorithm (GA)\u0000optimization neural network, and the improved PSO neural network are compared by considering\u0000various factors, such as ambient temperature, load changes, and the number of cooler groups under\u0000different working conditions. Results show that the proposed algorithm improves the actual change\u0000trend of oil surface temperature and makes the transformer operation more stable to a certain extent.\u0000\u0000\u0000\u0000The mathematical model for predicting transformer oil temperature is clear, but the\u0000parameters in the model are uncertain and vary with time. When subjected to different operating\u0000conditions, such as ambient temperature, load changes, and the number of cooler groups acting independently or in combination, the prediction results of the oil temperature model vary with different system parameters.\u0000\u0000\u0000\u0000This paper aims to enhance the accuracy of transformer temperature prediction. In order\u0000to optimize the oil temperature prediction model, asymmetric adjustment learning factors and mutant operators are added to meet diverse system parameter requirements.\u0000\u0000\u0000\u0000The paper utilizes an oil temperature prediction model based on an improved PSO neural\u0000network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator to address the limitations of the standard PSO algorithm.\u0000\u0000\u0000\u0000This paper has employed a fusion algorithm of the genetic algorithm of the BP neural\u0000network and the PSO algorithm, and conducted simulation and experimental analysis. The simulation and experimental results demonstrate the accuracy and effectiveness of the fusion algorithm.\u0000\u0000\u0000\u0000This study demonstrates enhanced prediction accuracy of transformer oil surface temperature using the improved particle swarm optimization neural network algorithm. This algorithm\u0000has less prediction error under different working conditions compared to other algorithms. By increasing population diversity and combining inertia weights, the algorithm not only greatly improves its search performance but also avoids local optimization.\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"73 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76581753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-27DOI: 10.2174/2352096516666230427122716
Ajay Kumar, Deepak Kumar Gupta, Sriparna roy Ghatak
An investigation of Automatic Generation Control (AGC) for a two-area, multi-source, interconnected power system under deregulation is presented in this article. For a more realistic approach, physical constraints namely Generation Rate Constraints (GRC) and Time Delay (TD) are incorporated into the system. This article proposed a novel hybrid Improved Gravitational Search Algorithm – Binary Particle Search Optimization (IGSA-BPSO) optimized Fractional Order Proportional-Integral-Derivative (FOPID) controller to regulate the frequency of a multi-area multi-source (thermal-hydro-gas) interconnected power system in a deregulated environment. Integral Time Multiplied by Absolute Error (ITAE) is used as the objective function to be minimized by optimization techniques for getting optimum parameters of FOPID controllers installed in each area. To inspect the efficacy of the suggested method, the dynamics of the system are investigated for poolco, bilateral and contract violation cases and the comparative results are also presented and analyzed. The supremacy of the recommended technique is studied by comparing with other well-known techniques namely GSA and PSO. The robustness of the proposed system is examined by sensitivity analysis after variations in different system parameters. In this paper, the AC-DC tie-line model is incorporated for the AGC mechanism. Dynamic load changes condition is also tested and verified. The study found that the proposed controller provides improved system dynamics in all competitive electricity market contract situations under varied system uncertainties
{"title":"A Novel Hybrid IGSA-BPSO Optimized FOPID Controller for Load Frequency Control of multi-source Restructured Power System","authors":"Ajay Kumar, Deepak Kumar Gupta, Sriparna roy Ghatak","doi":"10.2174/2352096516666230427122716","DOIUrl":"https://doi.org/10.2174/2352096516666230427122716","url":null,"abstract":"\u0000\u0000An investigation of Automatic Generation Control (AGC) for a two-area, multi-source, interconnected power system under deregulation is presented in this article. For a more realistic approach, physical constraints namely Generation Rate Constraints (GRC) and Time Delay (TD) are incorporated into the system.\u0000\u0000\u0000\u0000This article proposed a novel hybrid Improved Gravitational Search Algorithm – Binary Particle Search Optimization (IGSA-BPSO) optimized Fractional Order Proportional-Integral-Derivative (FOPID) controller to regulate the frequency of a multi-area multi-source (thermal-hydro-gas) interconnected power system in a deregulated environment.\u0000\u0000\u0000\u0000Integral Time Multiplied by Absolute Error (ITAE) is used as the objective function to be minimized by optimization techniques for getting optimum parameters of FOPID controllers installed in each area.\u0000\u0000\u0000\u0000To inspect the efficacy of the suggested method, the dynamics of the system are investigated for poolco, bilateral and contract violation cases and the comparative results are also presented and analyzed. The supremacy of the recommended technique is studied by comparing with other well-known techniques namely GSA and PSO.\u0000\u0000\u0000\u0000The robustness of the proposed system is examined by sensitivity analysis after variations in different system parameters. In this paper, the AC-DC tie-line model is incorporated for the AGC mechanism. Dynamic load changes condition is also tested and verified. The study found that the proposed controller provides improved system dynamics in all competitive electricity market contract situations under varied system uncertainties\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"36 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90908875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-27DOI: 10.2174/2352096516666230427141327
Karamjit Kaur, Rujeko Masike, R. Arora, S.N. Shridhara
The advancements in robotic technology have completely revolutionized day-to-day life. In industrial applications, the implementation of robotics is quite advantageous as it may help in performing dangerous tasks like climbing high walls, working in a high-temperature environment, high radiation exposure conditions etc This paper presents the design and development of a wall-climbing robot for dam wall inspection using an adaptive aerodynamic adhesion technique. The optimization of a robot design is done using a differential evolutionary algorithm. In the proposed model, the principle of Bernoulli adhesion is used for designing the suction pad. The optimization of various variables is done using a differential evolutionary algorithm to improve the efficiency and effectiveness of the wall climbing robot adhesion. The results of the proposed system show that the approach can find an optimal holding force and can be effectively used for applications like dam wall climbing for inspection.
{"title":"Optimization of Holding Force for a Climbing Robot Based on a Differential Evolutionary Algorithm","authors":"Karamjit Kaur, Rujeko Masike, R. Arora, S.N. Shridhara","doi":"10.2174/2352096516666230427141327","DOIUrl":"https://doi.org/10.2174/2352096516666230427141327","url":null,"abstract":"\u0000\u0000The advancements in robotic technology have completely revolutionized\u0000day-to-day life. In industrial applications, the implementation of robotics is quite advantageous as\u0000it may help in performing dangerous tasks like climbing high walls, working in a high-temperature\u0000environment, high radiation exposure conditions etc\u0000\u0000\u0000\u0000This paper presents the design and development of a wall-climbing robot for dam wall inspection using an adaptive aerodynamic adhesion technique. The optimization of a robot design is\u0000done using a differential evolutionary algorithm.\u0000\u0000\u0000\u0000In the proposed model, the principle of Bernoulli adhesion is used for designing the suction\u0000pad. The optimization of various variables is done using a differential evolutionary algorithm to\u0000improve the efficiency and effectiveness of the wall climbing robot adhesion.\u0000\u0000\u0000\u0000The results of the proposed system show that the approach can find an optimal holding force and can be effectively used for applications like dam wall climbing for inspection.\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"41 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88576763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-27DOI: 10.2174/2352096516666230427142102
Achraf Nouri, Aymen Lachheb, L. El Amraoui
This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a linear quadratic regulator (LQR) combined with an artificial neural network (ANN) algorithm. A state representation of the Buck-boost converter is performed. Then the ANN-LQR control strategy is compared to a classical control based on the proportional-integral controller combined with an ANN algorithm. The ANN algorithm generates the reference charging or discharging current based on a comparison between the power generated and the power consumed. In order to obtain an accurate comparison, two identical systems are designed, each consisting of a photovoltaic system optimized by an incremental conductance algorithm (INC) that powers a dynamic load and a backup storage system consisting of a lithium-ion battery. A management and protection algorithm is developed to protect the batteries from overcharge and deep discharge and to manage the load availability on the DC bus. The simulation results show an improvement in the performances of the storage system by the ANN-LQR control compared to the ANN-PI method and an increase in the stability, accuracy, efficiency of the system is observed. Photovoltaic (PV) energy is one of the most promising technologies for combating climate change and meeting the urgent need for green renewable energy and long-term development. PV energy generation has a number of advantages: Solar energy is limitless and available anywhere on the planet. However, photovoltaic energy is intermittent and depends on meteorological conditions; also, the energy consumed is unpredictable. For this reason, a storage system is necessary to overcome these problems. The objective of this study is to develop an optimal control using a Linear Quadratic Regulator (LQR) combined with a neural network algorithm (ANN) to improve the performance of an electrical energy storage system and compare the results obtained with the classical control based on the PI regulator. The state representation of the bidirectional Buck-boost converter was performed in order to apply the optimal control and determine the gain K and the ANN algorithm allowed to determine the charge and discharge current according to a comparison between the power produced and consumed. The simulation results obtained by two control methods can be used to compare and select the appropriate control method to achieve optimal efficiency of the storage system. The combined ANN-LQR technique offer better performances and stability of the installation compared to the ANN-PI controller.
{"title":"A Comparative Study of the Performances of the LQR Regulator versus the PI Regulator for the Control of a Battery Storage System","authors":"Achraf Nouri, Aymen Lachheb, L. El Amraoui","doi":"10.2174/2352096516666230427142102","DOIUrl":"https://doi.org/10.2174/2352096516666230427142102","url":null,"abstract":"\u0000\u0000This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a\u0000linear quadratic regulator (LQR) combined with an artificial neural network (ANN) algorithm. A\u0000state representation of the Buck-boost converter is performed. Then the ANN-LQR control strategy\u0000is compared to a classical control based on the proportional-integral controller combined with an\u0000ANN algorithm. The ANN algorithm generates the reference charging or discharging current based\u0000on a comparison between the power generated and the power consumed. In order to obtain an accurate comparison, two identical systems are designed, each consisting of a photovoltaic system optimized by an incremental conductance algorithm (INC) that powers a dynamic load and a backup\u0000storage system consisting of a lithium-ion battery. A management and protection algorithm is developed to protect the batteries from overcharge and deep discharge and to manage the load availability on the DC bus. The simulation results show an improvement in the performances of the storage system by the ANN-LQR control compared to the ANN-PI method and an increase in the stability, accuracy, efficiency of the system is observed.\u0000\u0000\u0000\u0000Photovoltaic (PV) energy is one of the most promising technologies for combating\u0000climate change and meeting the urgent need for green renewable energy and long-term development. PV energy generation has a number of advantages: Solar energy is limitless and available\u0000anywhere on the planet. However, photovoltaic energy is intermittent and depends on meteorological conditions; also, the energy consumed is unpredictable. For this reason, a storage system is necessary to overcome these problems.\u0000\u0000\u0000\u0000The objective of this study is to develop an optimal control using a Linear Quadratic\u0000Regulator (LQR) combined with a neural network algorithm (ANN) to improve the performance of\u0000an electrical energy storage system and compare the results obtained with the classical control\u0000based on the PI regulator.\u0000\u0000\u0000\u0000The state representation of the bidirectional Buck-boost converter was performed in order\u0000to apply the optimal control and determine the gain K and the ANN algorithm allowed to determine\u0000the charge and discharge current according to a comparison between the power produced and consumed.\u0000\u0000\u0000\u0000The simulation results obtained by two control methods can be used to compare and select\u0000the appropriate control method to achieve optimal efficiency of the storage system.\u0000\u0000\u0000\u0000The combined ANN-LQR technique offer better performances and stability of the installation compared to the ANN-PI controller.\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"39 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79863391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-20DOI: 10.2174/2352096516666230420081217
Sanjeev P. Kaulgud, Vishwanath R. Hulipalled, S. Patil, Prabhuraj Metipatil
In recent periods, micro-array data analysis using soft computing and machine learning techniques gained more interest among researchers to detect prostate cancer. Due to the small sample size of micro-array data with a larger number of attributes, traditional machine learning techniques face difficulty detecting prostate cancer. The selection of relevant genes exploits useful information about micro-array data, which enhances the accuracy of detection. In this research, the samples are acquired from the gene expression omnibus database, particularly related to the prostate cancer GEO IDs such as GSE 21034, GSE 15484 and GSE 3325/GSE 3998. In addition, ensemble feature optimization technique and Bidirectional Long Short Term Memory (Bi-LSTM) network are employed for detecting prostate cancer from the microarray data of gene expression. The ensemble feature optimization technique includes 4 metaheuristic optimizers that select the top 2000 genes from each GEO IDs, which are relevant to prostate cancer. Next, the selected genes are given to the Bi-LSTM network for classifying the normal and prostate cancer subjects. The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484. The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484.
{"title":"Detection of prostate cancer using ensemble based bi-directional long short term memory network","authors":"Sanjeev P. Kaulgud, Vishwanath R. Hulipalled, S. Patil, Prabhuraj Metipatil","doi":"10.2174/2352096516666230420081217","DOIUrl":"https://doi.org/10.2174/2352096516666230420081217","url":null,"abstract":"\u0000\u0000In recent periods, micro-array data analysis using soft computing and machine learning techniques gained more interest among researchers to detect prostate cancer. Due to the small sample size of micro-array data with a larger number of attributes, traditional machine learning techniques face difficulty detecting prostate cancer.\u0000\u0000\u0000\u0000The selection of relevant genes exploits useful information about micro-array data, which enhances the accuracy of detection. In this research, the samples are acquired from the gene expression omnibus database, particularly related to the prostate cancer GEO IDs such as GSE 21034, GSE 15484 and GSE 3325/GSE 3998. In addition, ensemble feature optimization technique and Bidirectional Long Short Term Memory (Bi-LSTM) network are employed for detecting prostate cancer from the microarray data of gene expression.\u0000\u0000\u0000\u0000The ensemble feature optimization technique includes 4 metaheuristic optimizers that select the top 2000 genes from each GEO IDs, which are relevant to prostate cancer. Next, the selected genes are given to the Bi-LSTM network for classifying the normal and prostate cancer subjects.\u0000\u0000\u0000\u0000The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484.\u0000\u0000\u0000\u0000The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484.\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"149 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73295520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-20DOI: 10.2174/2352096516666230420090225
Shruti Jain, Anupama Jamwal
Medical imaging requires special operating procedures and can cause mis-images that occur when someone is getting imaged, which can lead to inaccurate results Adaptive illustration of the signal is imperative in signal processing. Empirical Wavelet Transform (EWT) is a new-fangled adaptive signal decomposition technique. Brain image fusion understands a dynamic job in medical imaging applications by assisting radiologists in detecting the variation in CT and MR images. This paper presents a fusion of filter banks of CT-MR image modalities of the Brain using the Empirical Curvelet Transform and Hybrid technique. In the hybrid technique filter banks of CT curvelet-MR little wood and CT little wood -MR curvelet were fused. The images were preprocessed using the Top Hat transform technique. The evaluation was performed based on the performance evaluation parameter. PSNR and SSIM are considered performance evaluation parameters It has been observed that the results of fused filter banks using the curvelet technique show remarkable results in terms of PSNR and SSIM. The fused results show 29.10 dB PSNR and 0.819 SSIM. It has been observed that the fusion using only curvelet results in a 47.25% improvement in comparison with CT curvelet-MR little wood and a 42.68% improvement in comparison with CT little wood -MR curvelet. -
{"title":"Curvempirical Transform for Multimodal fusion of Brain Images","authors":"Shruti Jain, Anupama Jamwal","doi":"10.2174/2352096516666230420090225","DOIUrl":"https://doi.org/10.2174/2352096516666230420090225","url":null,"abstract":"\u0000\u0000Medical imaging requires special operating procedures and can cause mis-images\u0000that occur when someone is getting imaged, which can lead to inaccurate results\u0000\u0000\u0000\u0000Adaptive illustration of the signal is imperative in signal processing. Empirical\u0000Wavelet Transform (EWT) is a new-fangled adaptive signal decomposition technique.\u0000\u0000\u0000\u0000Brain image fusion understands a dynamic job in medical imaging applications by assisting radiologists in detecting the variation in CT and MR images.\u0000\u0000\u0000\u0000This paper presents a fusion of filter banks of CT-MR image modalities of the Brain using the Empirical Curvelet Transform and Hybrid technique. In the hybrid technique filter banks\u0000of CT curvelet-MR little wood and CT little wood -MR curvelet were fused. The images were preprocessed using the Top Hat transform technique. The evaluation was performed based on the\u0000performance evaluation parameter. PSNR and SSIM are considered performance evaluation parameters\u0000\u0000\u0000\u0000It has been observed that the results of fused filter banks using the curvelet technique\u0000show remarkable results in terms of PSNR and SSIM. The fused results show 29.10 dB PSNR and\u00000.819 SSIM.\u0000\u0000\u0000\u0000It has been observed that the fusion using only curvelet results in a 47.25% improvement in comparison with CT curvelet-MR little wood and a 42.68% improvement in comparison with CT little wood -MR curvelet.\u0000\u0000\u0000\u0000-\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"61 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74628446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-19DOI: 10.2174/2352096516666230419102435
Shruti Jain, Sudip Paul, Kshitij Sharma
Millions of neurons make up the human brain, and they play an important role in controlling the body's response to internal and external motor and sensory stimuli. These neurons can function as contact conduits between the human body and the brain. Analyzing brain signals or photographs will help one better understand cognitive function. These states are linked to a particular signal frequency that aids in the comprehension of how a complex brain system works. Electroencephalography (EEG) is a useful method for locating brain waves associated with different countries on the scalp. Epilepsy is a condition where the brain or some part of it is overactive and sends too many signals. This results in seizures causing muscles to twitch or whole-body convulsions. In this paper, the author has designed a model to predict epilepsy using machine learning algorithms and deep learning models. For the machine learning algorithm, different features were extracted and a particle swarm optimization algorithm was used to select the best feature which was classified using wavelet transform.Vgg16, Vgg19, and Inception V3 models are used for the detection of epilepsy. The inception V3 model results in 97.87% accuracy which is better than all other techniques. 5.1% accuracy improvement has been observed using a machine learning algorithm. The model is compared using existing work and it has been observed that the proposed model results better. The technique for modeling EEG signals and insight brain signals recorded during surgical procedures has been identified in detail. 0.7% and 0.13% accuracy improvement were achieved when the model is validated on Kaggle and CHB-MIT datasets respectively.
{"title":"EEG brain signal processing for epilepsy detection","authors":"Shruti Jain, Sudip Paul, Kshitij Sharma","doi":"10.2174/2352096516666230419102435","DOIUrl":"https://doi.org/10.2174/2352096516666230419102435","url":null,"abstract":"\u0000\u0000Millions of neurons make up the human brain, and they play an important role in controlling the body's response to internal and external motor and sensory stimuli. These neurons can function as contact conduits between the human body and the brain. Analyzing brain signals or photographs will help one better understand cognitive function. These states are linked to a particular signal frequency that aids in the comprehension of how a complex brain system works.\u0000\u0000\u0000\u0000Electroencephalography (EEG) is a useful method for locating brain waves associated with different countries on the scalp. Epilepsy is a condition where the brain or some part of it is overactive and sends too many signals. This results in seizures causing muscles to twitch or whole-body convulsions.\u0000\u0000\u0000\u0000In this paper, the author has designed a model to predict epilepsy using machine learning algorithms and deep learning models. For the machine learning algorithm, different features were extracted and a particle swarm optimization algorithm was used to select the best feature which was classified using wavelet transform.Vgg16, Vgg19, and Inception V3 models are used for the detection of epilepsy.\u0000\u0000\u0000\u0000The inception V3 model results in 97.87% accuracy which is better than all other techniques. 5.1% accuracy improvement has been observed using a machine learning algorithm. The model is compared using existing work and it has been observed that the proposed model results better.\u0000\u0000\u0000\u0000The technique for modeling EEG signals and insight brain signals recorded during surgical procedures has been identified in detail. 0.7% and 0.13% accuracy improvement were achieved when the model is validated on Kaggle and CHB-MIT datasets respectively.\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"36 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82235176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.2174/2352096516666230418113306
Minnan Wang, Honggang Wang, Anqing Chen, Yangqi Yu, Ge Xiao
With the in-depth study of microgrid system planning and operation strategy, comprehensive management and effective evaluation of various planning schemes and implementation effects are required. A comprehensive evaluation method based on the elasticity coefficient analytic hierarchy process (EC-AHP) and triangle fuzzy number is proposed. Meanwhile, considering the changes in the internal and external environment of the system, a comprehensive evaluation index system for a microgrid with the coordinated operation of "source-grid-load-storage" is constructed combined with the existing microgrid planning and operation evaluation index system. The EC-AHP method avoids the limitation of using the "1-9" scaling method to determine the judgment matrix in the traditional AHP method, and uses the elasticity coefficient to reflect the degree of influence of the index changes on the expert's score, which reflects the importance of the index for the evaluation result. Introducing triangular fuzzy numbers to the evaluation of microgrid planning and operation can provide a comprehensive linguistic rating for evaluation indexes and systems Through applying the constructed evaluation index system and evaluation method to the calculation example of a regional microgrid, the pros and cons of various evaluation indexes of the microgrid and the weak links in the planning and operation process are obtained. The results show that the index system and evaluation method constructed in this paper can provide a basis for the improvement of microgrid planning and operation strategies, which can then be applied to the general management of the microgrid system.
{"title":"Low-Carbon Economic Assessment of Microgrid Based on EC-AHP and Triangular Fuzzy Number","authors":"Minnan Wang, Honggang Wang, Anqing Chen, Yangqi Yu, Ge Xiao","doi":"10.2174/2352096516666230418113306","DOIUrl":"https://doi.org/10.2174/2352096516666230418113306","url":null,"abstract":"\u0000\u0000With the in-depth study of microgrid system planning and operation strategy,\u0000comprehensive management and effective evaluation of various planning schemes and implementation effects are required.\u0000\u0000\u0000\u0000A comprehensive evaluation method based on the elasticity coefficient analytic\u0000hierarchy process (EC-AHP) and triangle fuzzy number is proposed. Meanwhile, considering the\u0000changes in the internal and external environment of the system, a comprehensive evaluation index\u0000system for a microgrid with the coordinated operation of \"source-grid-load-storage\" is constructed\u0000combined with the existing microgrid planning and operation evaluation index system.\u0000\u0000\u0000\u0000The EC-AHP method avoids the limitation of using the \"1-9\" scaling method to determine the judgment matrix in the traditional AHP method, and uses the elasticity coefficient to\u0000reflect the degree of influence of the index changes on the expert's score, which reflects the importance of the index for the evaluation result. Introducing triangular fuzzy numbers to the evaluation of microgrid planning and operation can provide a comprehensive linguistic rating for\u0000evaluation indexes and systems\u0000\u0000\u0000\u0000Through applying the constructed evaluation index system and evaluation method to\u0000the calculation example of a regional microgrid, the pros and cons of various evaluation indexes\u0000of the microgrid and the weak links in the planning and operation process are obtained.\u0000\u0000\u0000\u0000The results show that the index system and evaluation method constructed in this\u0000paper can provide a basis for the improvement of microgrid planning and operation strategies,\u0000which can then be applied to the general management of the microgrid system.\u0000","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"4 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90868344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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