Pub Date : 2023-09-02DOI: 10.37936/ecti-cit.2023173.251670
Rupa Rajakumari R Peter, Ujwal Ambadas Lanjewar
Air quality is a topic that has been of utmost concern across the globe for the past few decades. Various intelligent monitoring systems are used in diverse scenarios, collecting air quality data that contains missing values. Such missing values in data cause hindrances in forecasting. This time series prediction or forecasting process extracts the necessary information from historical records and predicts future values. To solve the missing values issue in data, Generative Adversarial Networks (GAN) are used to impute the missed data. While the learning of long-term dependencies embedded in the time series poses another threat to the models in the time prediction. To overcome this, Long Short-Term Memory (LSTM) models are used. Yet, most of the neural network-based methods failed to consider the patterns of time series data that varied for each period, and the encoder-decoder performance deteriorated for longer sequences. To combat this, the present study proposes a hybrid probabilistic model to generate parameters for predictive distribution at every step. Hence, an implementation of hierarchical-attention-based BiLSTM with GAN is proposed in the study for effective prediction and minimal error. The proposed model is assessed with the evaluation metrics such as Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Square Error (MSE). The evaluation metric confirmed the higher accuracy of the proposed model than the existing models in time series prediction.
{"title":"Improving Air Quality Prediction with a Hybrid Bi-LSTM and GAN Model","authors":"Rupa Rajakumari R Peter, Ujwal Ambadas Lanjewar","doi":"10.37936/ecti-cit.2023173.251670","DOIUrl":"https://doi.org/10.37936/ecti-cit.2023173.251670","url":null,"abstract":"Air quality is a topic that has been of utmost concern across the globe for the past few decades. Various intelligent monitoring systems are used in diverse scenarios, collecting air quality data that contains missing values. Such missing values in data cause hindrances in forecasting. This time series prediction or forecasting process extracts the necessary information from historical records and predicts future values. To solve the missing values issue in data, Generative Adversarial Networks (GAN) are used to impute the missed data. While the learning of long-term dependencies embedded in the time series poses another threat to the models in the time prediction. To overcome this, Long Short-Term Memory (LSTM) models are used. Yet, most of the neural network-based methods failed to consider the patterns of time series data that varied for each period, and the encoder-decoder performance deteriorated for longer sequences. To combat this, the present study proposes a hybrid probabilistic model to generate parameters for predictive distribution at every step. Hence, an implementation of hierarchical-attention-based BiLSTM with GAN is proposed in the study for effective prediction and minimal error. The proposed model is assessed with the evaluation metrics such as Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Square Error (MSE). The evaluation metric confirmed the higher accuracy of the proposed model than the existing models in time series prediction.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87145298","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 : 2023-08-11DOI: 10.37936/ecti-cit.2023173.252549
A. Chakraborty, Dipankar Das, A. Kolya
Millions of lives were affected rapidly throughout the world when the Covid-19 outbreak spread by leaps and bounds. During this catastrophic period, people used to express their condolence as well as emotions through different social networks. In order to analyze the public comments on Twitter, an experimental approach is developed based on popular words regarding this pandemic. In this paper, various NLP-based research works are discussed on sentiment analysis, trend prediction, topic modeling, learning mechanisms, etc. Furthermore, the hybrid deep learning models are developed based on the Naïve Bayes sentiment model to predict the sentiment from the collected huge number of Coronavirus-related tweets. After performing the n-gram analysis, the Covid-19 specific words are extracted based on their popularity. The public sentiment trend has been analyzed using the extracted topics related to Covid-19 and the tweets are classified according to their sentiment scores. The distinguished sentiment ratings are assigned to the collected tweets based on their sentiment class. Then Convo-Sequential and Convo-Bidirectional long-short term networks are trained using fine-grained sentiment-rated tweets to categorize Covid-19 tweets into five different sentiment classes. Finally, our proposed Convo-Sequential and Convo-Bidirectional LSTM models achieved 84.52% and 85.03% of validation accuracy respectively for the first phase dataset whereas using the second phase dataset the models obtained the validation accuracy of 86.58% and 87.22% respectively.
{"title":"Sentiment Analysis on Large-Scale Covid-19 Tweets using Hybrid Convolutional LSTM Based on Naïve Bayes Sentiment Modeling","authors":"A. Chakraborty, Dipankar Das, A. Kolya","doi":"10.37936/ecti-cit.2023173.252549","DOIUrl":"https://doi.org/10.37936/ecti-cit.2023173.252549","url":null,"abstract":"Millions of lives were affected rapidly throughout the world when the Covid-19 outbreak spread by leaps and bounds. During this catastrophic period, people used to express their condolence as well as emotions through different social networks. In order to analyze the public comments on Twitter, an experimental approach is developed based on popular words regarding this pandemic. In this paper, various NLP-based research works are discussed on sentiment analysis, trend prediction, topic modeling, learning mechanisms, etc. Furthermore, the hybrid deep learning models are developed based on the Naïve Bayes sentiment model to predict the sentiment from the collected huge number of Coronavirus-related tweets. After performing the n-gram analysis, the Covid-19 specific words are extracted based on their popularity. The public sentiment trend has been analyzed using the extracted topics related to Covid-19 and the tweets are classified according to their sentiment scores. The distinguished sentiment ratings are assigned to the collected tweets based on their sentiment class. Then Convo-Sequential and Convo-Bidirectional long-short term networks are trained using fine-grained sentiment-rated tweets to categorize Covid-19 tweets into five different sentiment classes. Finally, our proposed Convo-Sequential and Convo-Bidirectional LSTM models achieved 84.52% and 85.03% of validation accuracy respectively for the first phase dataset whereas using the second phase dataset the models obtained the validation accuracy of 86.58% and 87.22% respectively.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"169 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74308222","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 : 2023-07-22DOI: 10.37936/ecti-cit.2023173.251272
S. S, C. Jeyalakshmi
Recommender Systems (RSs) aid in filtering information seeking to envisage user and item ratings, primarily from huge data to recommend the likes. Movie RSs offer a scheme to help users categorize them based on comparable interests. This enables RSs to be a dominant part of websites and e-commerce applications. This paper proposes an optimized RS for movies, primarily aiming to suggest an RS by clustering data and Computational Intelligence (CI). Unsupervised clustering, a model-based Collaborative Filtering (CF) category, is preferred as it offers simple and practical recommendations. Nevertheless, it involves an increased error rate and consumes more iterations for converging. Enhanced Fuzzy C-Means (EFCM) clustering is proposed to handle these issues. Dove Swarm Optimisation Algorithm (DSOA)-based RS is proposed for optimising Data Points (DPs) in every cluster, providing effcient recommendations. The performance of the proposed EFCM-DSOA-based RS is analysed by performing an experimental study on benchmarked MovieLens Dataset. To ensure the effciency of the proposed EFCM-DSOA-based RS, the outcomes are compared with EFCM-Particle Swarm Optimization (EFCM-PSO) and EFCM-Cuckoo Search (EFCM-CS) based on standard optimization functions. The proposed EFCM-DSOA-based RS offers improved F-measure, Accuracy, and Fitness convergence.
{"title":"Collaborative Movie Recommendation System using Enhanced Fuzzy C-Means Clustering with Dove Swarm Optimization Algorithm","authors":"S. S, C. Jeyalakshmi","doi":"10.37936/ecti-cit.2023173.251272","DOIUrl":"https://doi.org/10.37936/ecti-cit.2023173.251272","url":null,"abstract":"Recommender Systems (RSs) aid in filtering information seeking to envisage user and item ratings, primarily from huge data to recommend the likes. Movie RSs offer a scheme to help users categorize them based on comparable interests. This enables RSs to be a dominant part of websites and e-commerce applications. This paper proposes an optimized RS for movies, primarily aiming to suggest an RS by clustering data and Computational Intelligence (CI). Unsupervised clustering, a model-based Collaborative Filtering (CF) category, is preferred as it offers simple and practical recommendations. Nevertheless, it involves an increased error rate and consumes more iterations for converging. Enhanced Fuzzy C-Means (EFCM) clustering is proposed to handle these issues. Dove Swarm Optimisation Algorithm (DSOA)-based RS is proposed for optimising Data Points (DPs) in every cluster, providing effcient recommendations. The performance of the proposed EFCM-DSOA-based RS is analysed by performing an experimental study on benchmarked MovieLens Dataset. To ensure the effciency of the proposed EFCM-DSOA-based RS, the outcomes are compared with EFCM-Particle Swarm Optimization (EFCM-PSO) and EFCM-Cuckoo Search (EFCM-CS) based on standard optimization functions. The proposed EFCM-DSOA-based RS offers improved F-measure, Accuracy, and Fitness convergence.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"129 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79583558","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 : 2023-07-22DOI: 10.37936/ecti-cit.2023173.251829
Tasiransurini Ab Rahman, Nor Azlina Ab. Aziz, Z. Ibrahim
Asynchronous Finite Impulse Response Optimizer (AFIRO) is a metaheuristic algorithm that has been developed as a population-based solution with an asynchronous update mechanism. AFIRO is inspired by the Ultimate Unbiased Finite Impulse Response filter framework. AFIRO works with a group of agents where each agent performs the iteration update asynchronously. In the original paper, AFIRO was compared with the Particle Swarm Optimisation algorithm, Genetic Algorithm, and Grey Wolf Optimizer. Although AFIRO shows a better performance, the comparison seems unfair since the iteration strategy of AFIRO is different from those compared algorithms. Hence, this article further investigates the potential of AFIRO against three existent metaheuristic algorithms with the same iteration strategy, namely Asynchronous PSO (A-PSO), Asynchronous Gravitational Search Algorithm (A-GSA), and Asynchronous Simulated Kalman Filter (A-SKF). The CEC2014 test suite was applied to evaluate the performance, where the results revealed that AFIRO leads 18 out of 30 functions. The Holm post hoc showed that AFIRO performs significantly better than A-SKF and A-GSA while having the same performance as A- PSO. Moreover, the Friedman test disclosed that AFIRO has the highest ranking than A-PSO, A-SKF, and A-GSA. Therefore, it can be concluded that AFIRO performs well in the same iteration strategy category.
{"title":"A Performance of AFIRO among Asynchronous Iteration Strategy Metaheuristic Algorithms","authors":"Tasiransurini Ab Rahman, Nor Azlina Ab. Aziz, Z. Ibrahim","doi":"10.37936/ecti-cit.2023173.251829","DOIUrl":"https://doi.org/10.37936/ecti-cit.2023173.251829","url":null,"abstract":"Asynchronous Finite Impulse Response Optimizer (AFIRO) is a metaheuristic algorithm that has been developed as a population-based solution with an asynchronous update mechanism. AFIRO is inspired by the Ultimate Unbiased Finite Impulse Response filter framework. AFIRO works with a group of agents where each agent performs the iteration update asynchronously. In the original paper, AFIRO was compared with the Particle Swarm Optimisation algorithm, Genetic Algorithm, and Grey Wolf Optimizer. Although AFIRO shows a better performance, the comparison seems unfair since the iteration strategy of AFIRO is different from those compared algorithms. Hence, this article further investigates the potential of AFIRO against three existent metaheuristic algorithms with the same iteration strategy, namely Asynchronous PSO (A-PSO), Asynchronous Gravitational Search Algorithm (A-GSA), and Asynchronous Simulated Kalman Filter (A-SKF). The CEC2014 test suite was applied to evaluate the performance, where the results revealed that AFIRO leads 18 out of 30 functions. The Holm post hoc showed that AFIRO performs significantly better than A-SKF and A-GSA while having the same performance as A- PSO. Moreover, the Friedman test disclosed that AFIRO has the highest ranking than A-PSO, A-SKF, and A-GSA. Therefore, it can be concluded that AFIRO performs well in the same iteration strategy category.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87175776","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 : 2023-06-27DOI: 10.37936/ecti-eec.2023212.249824
D. Diana, R. Hema
The automatic upgradation of equalizer weights in channel equalization demands a low-complexity, highly accurate estimation of recovery at the minimum possible time. The low-complexity frequency domain equalization improves the minimum mean square error (MMSE) of the equalization process. Adding the superiority of particle swarm optimization (PSO) to the equalizer coefficient selection process enhances the MMSE. This work proposes frequency-domain channel equalization along with a modified PSO (MPSO) as an adaptive algorithm for equalizer weight selection in MIMO systems. The simulation results validate the performance with the time domain linear and decision feedback equalizer structures for BPSK and QAM systems. The parameters are carefully selected by analyzing MMSE thoroughly under timevarying channel conditions.
{"title":"Swarm Intelligence Based MMSE Frequency Domain Equalization for MIMO Systems","authors":"D. Diana, R. Hema","doi":"10.37936/ecti-eec.2023212.249824","DOIUrl":"https://doi.org/10.37936/ecti-eec.2023212.249824","url":null,"abstract":"The automatic upgradation of equalizer weights in channel equalization demands a low-complexity, highly accurate estimation of recovery at the minimum possible time. The low-complexity frequency domain equalization improves the minimum mean square error (MMSE) of the equalization process. Adding the superiority of particle swarm optimization (PSO) to the equalizer coefficient selection process enhances the MMSE. This work proposes frequency-domain channel equalization along with a modified PSO (MPSO) as an adaptive algorithm for equalizer weight selection in MIMO systems. The simulation results validate the performance with the time domain linear and decision feedback equalizer structures for BPSK and QAM systems. The parameters are carefully selected by analyzing MMSE thoroughly under timevarying channel conditions.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83512781","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 : 2023-06-27DOI: 10.37936/ecti-eec.2023212.249818
A. Al-Attabi, A. Al
Noise is unwanted electrical or electromagnetic radiation that degrades the quality of the signal and the data. It can be difficult to denoise a signal that has been acquired in a noisy environment, but doing so may be necessary in a number of signal processing applications. This paper extends the issue of signal denoising from signals with regular structures, which are affected by noise, to signals with irregular structures by applying the graph signal processing (GSP) technique and a very wellknown filter, the standard Kalman filter, after adjusting it. When the modified Kalman filter is compared to the standard Kalman filter, the modified one performs better in situations where there are uncertain observations and/or processing noise and shows the best results. Also, the modified Kalman filter showed a higher efficiency when we compared it with other filters for different types of noise, which are not only standard Gaussian noises but also uniform distribution noise across two intervals for uncertain observation noise.
{"title":"Spectral Graph Filtering for Noisy Signals Using the Kalman filter","authors":"A. Al-Attabi, A. Al","doi":"10.37936/ecti-eec.2023212.249818","DOIUrl":"https://doi.org/10.37936/ecti-eec.2023212.249818","url":null,"abstract":"Noise is unwanted electrical or electromagnetic radiation that degrades the quality of the signal and the data. It can be difficult to denoise a signal that has been acquired in a noisy environment, but doing so may be necessary in a number of signal processing applications. This paper extends the issue of signal denoising from signals with regular structures, which are affected by noise, to signals with irregular structures by applying the graph signal processing (GSP) technique and a very wellknown filter, the standard Kalman filter, after adjusting it. When the modified Kalman filter is compared to the standard Kalman filter, the modified one performs better in situations where there are uncertain observations and/or processing noise and shows the best results. Also, the modified Kalman filter showed a higher efficiency when we compared it with other filters for different types of noise, which are not only standard Gaussian noises but also uniform distribution noise across two intervals for uncertain observation noise.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76706156","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 : 2023-06-27DOI: 10.37936/ecti-eec.2023212.249826
L. Yong
Maximum demand (kW) has contributed significantly to expensive electricity bills. A modern-day solution for overcoming the penalty demand charges is to utilize the peak shaving method. To perform peak shaving, a battery storage system (BSS) is used. This methodinvolves the charging and discharging of the battery during high and low demand respectively, thus reducing the penalty incurred from the electricity utility company. To charge the battery, a photovoltaic (PV) system is coupled with the BSS. There is currently no BSS algorithm in existence under the microgrid to shave maximum demand with the aid of solar forecasting. In this paper, an algorithm for the BSS to achieve peak shave will be developed with the use of solar PV forecasting. The load profile of a building is used in this study as a reference for future consumption. The developed algorithm releases the energy stored in the BSS to shave the critical demand based on solar forecasting and the BSS state of charge (SOC). In short, this algorithm provides a green solution for reducing the demand charges from the electricity company.
{"title":"Peak Shaving Mechanism Employing a Battery Storage System (BSS) and Solar Forecasting","authors":"L. Yong","doi":"10.37936/ecti-eec.2023212.249826","DOIUrl":"https://doi.org/10.37936/ecti-eec.2023212.249826","url":null,"abstract":"Maximum demand (kW) has contributed significantly to expensive electricity bills. A modern-day solution for overcoming the penalty demand charges is to utilize the peak shaving method. To perform peak shaving, a battery storage system (BSS) is used. This methodinvolves the charging and discharging of the battery during high and low demand respectively, thus reducing the penalty incurred from the electricity utility company. To charge the battery, a photovoltaic (PV) system is coupled with the BSS. There is currently no BSS algorithm in existence under the microgrid to shave maximum demand with the aid of solar forecasting. In this paper, an algorithm for the BSS to achieve peak shave will be developed with the use of solar PV forecasting. The load profile of a building is used in this study as a reference for future consumption. The developed algorithm releases the energy stored in the BSS to shave the critical demand based on solar forecasting and the BSS state of charge (SOC). In short, this algorithm provides a green solution for reducing the demand charges from the electricity company.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"344 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75926434","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 : 2023-06-27DOI: 10.37936/ecti-eec.2023212.249822
K. Chayakulkheeree
In this paper, a method is proposed for coordinated optimal power dispatch (OPD) incorporating the scheduling of distributed energy resources (DERs (COPD-IDS). The proposed COPD-IDS aims to minimize the total daily operating cost of a power system by considering the optimal scheduling of DERs. In the problem formulation, the DERs are considered dispatchable limited energy units and treated as a virtual power plant (VPP). The OPD is solved for total hourly cost minimization, using quadratic programming (QP) as a subproblem in COPDIDS. Meanwhile, the total daily operating cost minimization incorporating the scheduling of DERs is solved by particle swarm optimization (PSO) and compared to a genetic algorithm (GA). The proposed COPD-IDS is tested on the modified IEEE 30-bus system under a practical load and the daily profiles of DERs. The simulation results show that the proposed method can minimize the total daily operational cost of the electricity system with the dispatchable condition of DERs using the VPP concept.
{"title":"Coordinated Optimal Power Dispatch Incorporating the Scheduling of Distributed Energy Resources Under the Virtual Power Plant Concept","authors":"K. Chayakulkheeree","doi":"10.37936/ecti-eec.2023212.249822","DOIUrl":"https://doi.org/10.37936/ecti-eec.2023212.249822","url":null,"abstract":"In this paper, a method is proposed for coordinated optimal power dispatch (OPD) incorporating the scheduling of distributed energy resources (DERs (COPD-IDS). The proposed COPD-IDS aims to minimize the total daily operating cost of a power system by considering the optimal scheduling of DERs. In the problem formulation, the DERs are considered dispatchable limited energy units and treated as a virtual power plant (VPP). The OPD is solved for total hourly cost minimization, using quadratic programming (QP) as a subproblem in COPDIDS. Meanwhile, the total daily operating cost minimization incorporating the scheduling of DERs is solved by particle swarm optimization (PSO) and compared to a genetic algorithm (GA). The proposed COPD-IDS is tested on the modified IEEE 30-bus system under a practical load and the daily profiles of DERs. The simulation results show that the proposed method can minimize the total daily operational cost of the electricity system with the dispatchable condition of DERs using the VPP concept.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88125491","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 : 2023-06-27DOI: 10.37936/ecti-eec.2023212.249809
Sorawin Phukapak, Daisuke Koyama, K. Hashikura, Md Kama, I. Murakami, K. Yamada
In this paper, we examine the parameterizations of all disturbance observers and all linear functional disturbance observers for periodic input disturbances. The plant disturbance observers have been used to estimate the disturbance in the plant. Several papers on design methods for disturbance observers have been published. Recently, the parameterization of all dis- turbance observers and all linear functional disturbance observers for plants with any input disturbance was clarified. However, no paper examines the parameteriza- tion of all disturbance observers or all linear functional disturbances for periodic input disturbances. In this paper, we propose parameterizations of all disturbance observers and all linear functional disturbance observers for periodic input disturbances.
{"title":"Parameterization of All Disturbance Observers for Periodic Input Disturbances","authors":"Sorawin Phukapak, Daisuke Koyama, K. Hashikura, Md Kama, I. Murakami, K. Yamada","doi":"10.37936/ecti-eec.2023212.249809","DOIUrl":"https://doi.org/10.37936/ecti-eec.2023212.249809","url":null,"abstract":"\u0000 \u0000 \u0000In this paper, we examine the parameterizations of all disturbance observers and all linear functional disturbance observers for periodic input disturbances. The plant disturbance observers have been used to estimate the disturbance in the plant. Several papers on design methods for disturbance observers have been published. Recently, the parameterization of all dis- turbance observers and all linear functional disturbance observers for plants with any input disturbance was clarified. However, no paper examines the parameteriza- tion of all disturbance observers or all linear functional disturbances for periodic input disturbances. In this paper, we propose parameterizations of all disturbance observers and all linear functional disturbance observers for periodic input disturbances. \u0000 \u0000 \u0000","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"T151 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82634375","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 : 2023-06-27DOI: 10.37936/ecti-eec.2023212.249820
C. Jeraputra, Somnida Bhatranand, Thamvarit Singhvilai, S. Tiptipakorn
Due to the rapid increase in single-phase inverters tied to the grid, fast and robust phase-locked loop algorithms have become indispensable. In previous work, a phase lead-lag synchronous reference frame phase-locked loop (PLL) was proposed. The method makes use of two single-tuned filters that perform as a phase detector. They are capable of shifting the phase of the grid voltage to be advanced or delayed by 45∘ with respect to the grid voltage phase. The generated orthogonal signals are transformed by Park transformation. The quadrature voltage is regulated to zero by means of a PI controller, while its output determines the frequency of the grid voltage and the phase angle obtained by integrating the estimated frequency. In this paper, deficiencies in the previous work are addressed. A small signal model of the method which takes into account frequency variation, voltage variation, and harmonic distortion is derived and presented. The design guidelines are discussed and an example illustrated. The method is validated through simulations and experiments under various voltage conditions while the algorithm is implemented on a rapid prototyping MicroLabBox. It is tested under different voltage scenarios, generated by a programmable AC source. The experimental results show that the method can track the phase angle of the grid voltage with nearly zero phase error under normal voltage conditions. It can track the phase of the grid voltage under 45∘ step phase jumps in 2.75 cycles, achieve harmonic attenuation of -15 dB under 15% third harmonic distortion, and attain an adequate phase margin near 45∘. This confirms that the method is fast and robust under adverse voltage conditions.
{"title":"Experimental Validation of a Phase Lead-Lag Synchronous Frame Phase-Locked Loop Under Different Voltage Conditions","authors":"C. Jeraputra, Somnida Bhatranand, Thamvarit Singhvilai, S. Tiptipakorn","doi":"10.37936/ecti-eec.2023212.249820","DOIUrl":"https://doi.org/10.37936/ecti-eec.2023212.249820","url":null,"abstract":"Due to the rapid increase in single-phase inverters tied to the grid, fast and robust phase-locked loop algorithms have become indispensable. In previous work, a phase lead-lag synchronous reference frame phase-locked loop (PLL) was proposed. The method makes use of two single-tuned filters that perform as a phase detector. They are capable of shifting the phase of the grid voltage to be advanced or delayed by 45∘ with respect to the grid voltage phase. The generated orthogonal signals are transformed by Park transformation. The quadrature voltage is regulated to zero by means of a PI controller, while its output determines the frequency of the grid voltage and the phase angle obtained by integrating the estimated frequency. In this paper, deficiencies in the previous work are addressed. A small signal model of the method which takes into account frequency variation, voltage variation, and harmonic distortion is derived and presented. The design guidelines are discussed and an example illustrated. The method is validated through simulations and experiments under various voltage conditions while the algorithm is implemented on a rapid prototyping MicroLabBox. It is tested under different voltage scenarios, generated by a programmable AC source. The experimental results show that the method can track the phase angle of the grid voltage with nearly zero phase error under normal voltage conditions. It can track the phase of the grid voltage under 45∘ step phase jumps in 2.75 cycles, achieve harmonic attenuation of -15 dB under 15% third harmonic distortion, and attain an adequate phase margin near 45∘. This confirms that the method is fast and robust under adverse voltage conditions.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79780999","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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