T. Ravi, K. Sathish Kumar, C. Dhanamjayulu, Baseem Khan
Power quality disturbances (PQDs) can lead to significant operational and financial losses in power systems. Accurate detection and classification of PQDs are essential for maintaining power quality and preventing power system failures. This research article introduces an innovative approach for the precise detection and classification of single- and multiple-state power quality disturbances (PQDs) using the Stockwell transform (ST) and a random forest classifier. To create realistic PQD signals, seventeen distinct classes are generated in accordance with IEEE Standard 1159, employing mathematical equations implemented in MATLAB software. The ST is employed to extract relevant features from the PQD signals, which are subsequently utilized as input for the random forest classifier. The classifier employs bootstrapping sampling to generate multiple training sets from the original dataset. Each training set is used to construct a decision tree by recursively partitioning the data based on significant features. To mitigate overfitting and enhance robustness, a random subset of features is selected at each node of the decision tree, thereby reducing tree correlation. The performance of the random forest classifier is compared with other widely utilized machine learning classifiers. The results exhibit the efficacy of the proposed approach in accurately detecting and classifying PQ events, highlighting its superiority over alternative methods.
{"title":"Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances","authors":"T. Ravi, K. Sathish Kumar, C. Dhanamjayulu, Baseem Khan","doi":"10.1155/2023/6615662","DOIUrl":"https://doi.org/10.1155/2023/6615662","url":null,"abstract":"Power quality disturbances (PQDs) can lead to significant operational and financial losses in power systems. Accurate detection and classification of PQDs are essential for maintaining power quality and preventing power system failures. This research article introduces an innovative approach for the precise detection and classification of single- and multiple-state power quality disturbances (PQDs) using the Stockwell transform (ST) and a random forest classifier. To create realistic PQD signals, seventeen distinct classes are generated in accordance with IEEE Standard 1159, employing mathematical equations implemented in MATLAB software. The ST is employed to extract relevant features from the PQD signals, which are subsequently utilized as input for the random forest classifier. The classifier employs bootstrapping sampling to generate multiple training sets from the original dataset. Each training set is used to construct a decision tree by recursively partitioning the data based on significant features. To mitigate overfitting and enhance robustness, a random subset of features is selected at each node of the decision tree, thereby reducing tree correlation. The performance of the random forest classifier is compared with other widely utilized machine learning classifiers. The results exhibit the efficacy of the proposed approach in accurately detecting and classifying PQ events, highlighting its superiority over alternative methods.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135301579","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}
Researchers are always looking for the improvement of existing methods. Today, CMOS technology is widely used, which has some advantages and disadvantages. One of the alternative methods for CMOS technology is QCA technology which compared to CMOS, has the advantages of low energy consumption and small occupied area. In this paper, by using the concepts and methods of QCA technology, a digital code converter is presented. In this converter, a new gate is used, which can produce outputs such as 4-input AND, 4-input OR, 4-input NAND, and 4-input NOR. The proposed converter has 10 inputs and 12 outputs. The 10 inputs are decimal numbers from 0 to 9, producing the output equivalent to excess-3, BCD, and gray codes. One of the advantages of this circuit is providing three different codes per input in just one circuit. In addition, due to the use of the new 4-input gate, the occupied area and the number of used cells were minimized. Simulations were performed by using QCADesigner-E version 2.2, and outcomes illustrated that the occupied area is equal to 0.29 μm2 and 380 QCA cells with 7 clock phases are used. The energy dissipation of the presented circuit is 171 meV. Also, given the favorable performance exhibited by the 4-input gate across various measurement parameters, it possesses the capability to be efficiently employed within larger and intricately designed circuits.
研究人员总是在寻找现有方法的改进。今天，CMOS技术被广泛应用，它有一些优点和缺点。QCA技术是CMOS技术的替代方法之一，与CMOS相比，QCA技术具有能耗低、占地面积小的优点。本文利用QCA技术的概念和方法，设计了一种数字编码转换器。在这个转换器中，使用了一个新的门，它可以产生4输入与输出、4输入或输出、4输入非与输出和4输入非与输出。该转换器具有10个输入和12个输出。10个输入是0到9之间的十进制数，产生的输出相当于过量-3、BCD和灰色编码。这种电路的优点之一是在一个电路中每个输入提供三种不同的代码。此外，由于使用了新的4输入栅极，占用的面积和使用的单元数量被最小化。采用qcaddesigner - e 2.2进行仿真，结果表明，占用面积为0.29 μm2，使用7个时钟相位的380个QCA细胞。该电路的能量损耗为171 meV。此外，考虑到四输入门在各种测量参数中表现出的良好性能，它具有在更大和复杂设计的电路中有效使用的能力。
{"title":"Design of a Novel Decimal to Multicode Converter in QCA Technology","authors":"Farhad Fouladinia, Mohammad Gholami","doi":"10.1155/2023/6615790","DOIUrl":"https://doi.org/10.1155/2023/6615790","url":null,"abstract":"Researchers are always looking for the improvement of existing methods. Today, CMOS technology is widely used, which has some advantages and disadvantages. One of the alternative methods for CMOS technology is QCA technology which compared to CMOS, has the advantages of low energy consumption and small occupied area. In this paper, by using the concepts and methods of QCA technology, a digital code converter is presented. In this converter, a new gate is used, which can produce outputs such as 4-input AND, 4-input OR, 4-input NAND, and 4-input NOR. The proposed converter has 10 inputs and 12 outputs. The 10 inputs are decimal numbers from 0 to 9, producing the output equivalent to excess-3, BCD, and gray codes. One of the advantages of this circuit is providing three different codes per input in just one circuit. In addition, due to the use of the new 4-input gate, the occupied area and the number of used cells were minimized. Simulations were performed by using QCADesigner-E version 2.2, and outcomes illustrated that the occupied area is equal to 0.29 μm2 and 380 QCA cells with 7 clock phases are used. The energy dissipation of the presented circuit is 171 meV. Also, given the favorable performance exhibited by the 4-input gate across various measurement parameters, it possesses the capability to be efficiently employed within larger and intricately designed circuits.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135253260","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}
{"title":"Retracted: Behavior of Sports Tourism Consumers Based on Cloud Computing and Mobile Big Data","authors":"Journal of Electrical and Computer Engineering","doi":"10.1155/2023/9804630","DOIUrl":"https://doi.org/10.1155/2023/9804630","url":null,"abstract":"<jats:p />","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590570","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}
H. V. Ramachandra, Pundalik Chavan, S. Supreeth, H. C. Ramaprasad, K. Chatrapathy, G. Balaraju, S. Rohith, H. S. Mohan
The development of the fifth generation (5G) and sixth generation (6G) wireless networks has gained wide spread importance in all aspects of life through the network due to their significantly higher speeds, extraordinarily low latency, and ubiquitous availability. Owing to the importance of their users, components, and services to our everyday lives, the network must secure all of these. With such a wide range of devices and service types being present in the 5G ecosystem, security issues are now much more prevalent. Security solutions, are not implemented, must already be envisioned in order to deal with a range of attacks on numerous services, cutting-edge technology, and more user information available over the network. This research proposes the dual integrated neural network (DINN) for secure data transmission in wireless networks. DINN comprises two neural networks based on sparse and dense dimensions. DINN is designed for any presence of deep learning-based attack in a physical security layer. DINN is evaluated considering the various machine learning attack such as basic_iterative_method attack, momentum_iterative_method attack, post_gradient_descent attack, and C&W attack; comparison is carried out on existing and DINN, considering attack success rate and MSE. Performance analysis suggests that DINN holds a higher level of security against the above attacks.
{"title":"Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture","authors":"H. V. Ramachandra, Pundalik Chavan, S. Supreeth, H. C. Ramaprasad, K. Chatrapathy, G. Balaraju, S. Rohith, H. S. Mohan","doi":"10.1155/2023/9390660","DOIUrl":"https://doi.org/10.1155/2023/9390660","url":null,"abstract":"The development of the fifth generation (5G) and sixth generation (6G) wireless networks has gained wide spread importance in all aspects of life through the network due to their significantly higher speeds, extraordinarily low latency, and ubiquitous availability. Owing to the importance of their users, components, and services to our everyday lives, the network must secure all of these. With such a wide range of devices and service types being present in the 5G ecosystem, security issues are now much more prevalent. Security solutions, are not implemented, must already be envisioned in order to deal with a range of attacks on numerous services, cutting-edge technology, and more user information available over the network. This research proposes the dual integrated neural network (DINN) for secure data transmission in wireless networks. DINN comprises two neural networks based on sparse and dense dimensions. DINN is designed for any presence of deep learning-based attack in a physical security layer. DINN is evaluated considering the various machine learning attack such as basic_iterative_method attack, momentum_iterative_method attack, post_gradient_descent attack, and C&W attack; comparison is carried out on existing and DINN, considering attack success rate and MSE. Performance analysis suggests that DINN holds a higher level of security against the above attacks.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135344498","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}
Implementing an efficient system for emotion recognition has recently posed a challenge that has not been fully developed yet. Facial emotion recognition (FER) is an important subject matter in the fields of artificial intelligence (AI) since it exhibits a greater commercial potential. This technique is used to analyse various sentiments and reveal a person’s behavior. It could be related to the mental or physiological state of mind. This paper mainly focuses on a human emotion recognition system through a detected human face. Its accuracy was improved via different data augmentation tools, early stopping, and generative adversarial networks (GANs). Compared to previous methods, experimental results show that the proposed method provides a 0.55% to 35.7% gain performance.
{"title":"Designing an Efficient System for Emotion Recognition Using CNN","authors":"Donia Ammous, Achraf Chabbouh, Awatef Edhib, Ahmed Chaari, Fahmi Kammoun, Nouri Masmoudi","doi":"10.1155/2023/9351345","DOIUrl":"https://doi.org/10.1155/2023/9351345","url":null,"abstract":"Implementing an efficient system for emotion recognition has recently posed a challenge that has not been fully developed yet. Facial emotion recognition (FER) is an important subject matter in the fields of artificial intelligence (AI) since it exhibits a greater commercial potential. This technique is used to analyse various sentiments and reveal a person’s behavior. It could be related to the mental or physiological state of mind. This paper mainly focuses on a human emotion recognition system through a detected human face. Its accuracy was improved via different data augmentation tools, early stopping, and generative adversarial networks (GANs). Compared to previous methods, experimental results show that the proposed method provides a 0.55% to 35.7% gain performance.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135393844","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 this paper, an intelligent integrated approach is proposed to control the reactive power and restore the voltage balance in a three-phase power system using particle swarm optimization (PSO), Gaussian process regression (GPR), and support vector machine (SVM). The PSO algorithm is used in offline mode to determine the optimal set of firing angles for the thyristor-controlled-reactor (TCR) compensator according to the smallest fitness value required for voltage balancing. The optimum firing angles are then used to train the GPR and SVM regression models. The GPR and SVM models are finally used as a real-time controller to retrieve the voltage balance in online mode. A simulation model and experimental setup of the electrical power system are built. The modeled system consists of a 500 km long transmission line. The line is divided into three-pi sections to guarantee a real system response. Several simulation and practical case studies have been conducted to test and validate the capability of the proposed integrated approach in solving the voltage unbalance problem. The results have revealed the supreme ability of the proposed integrated approach to restore the voltage balance quickly (within 20 ms) and for a wide range of voltage unbalance factors (VUFs) (3.90–8.42%).
{"title":"Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models","authors":"Jasim Ghaeb, Ibrahim Al-Naimi, Malek Alkayyali","doi":"10.1155/2023/8864216","DOIUrl":"https://doi.org/10.1155/2023/8864216","url":null,"abstract":"In this paper, an intelligent integrated approach is proposed to control the reactive power and restore the voltage balance in a three-phase power system using particle swarm optimization (PSO), Gaussian process regression (GPR), and support vector machine (SVM). The PSO algorithm is used in offline mode to determine the optimal set of firing angles for the thyristor-controlled-reactor (TCR) compensator according to the smallest fitness value required for voltage balancing. The optimum firing angles are then used to train the GPR and SVM regression models. The GPR and SVM models are finally used as a real-time controller to retrieve the voltage balance in online mode. A simulation model and experimental setup of the electrical power system are built. The modeled system consists of a 500 km long transmission line. The line is divided into three-pi sections to guarantee a real system response. Several simulation and practical case studies have been conducted to test and validate the capability of the proposed integrated approach in solving the voltage unbalance problem. The results have revealed the supreme ability of the proposed integrated approach to restore the voltage balance quickly (within 20 ms) and for a wide range of voltage unbalance factors (VUFs) (3.90–8.42%).","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135393574","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}
Jhon Francined Herrera-Cubides, Paulo Alonso Gaona-García, Carlos Enrique Montenegro-Marin, Salvador Sánchez-Alonso
Open data has been improving both publishing platforms and the consumers-oriented process over the years, providing better openness policies and transparency. Although organizations have tried to open their data, the enrichment of their resources through the Web of Data has been decreasing. Linked data has been suffering from notable difficulties in different stages of its life cycle, becoming over the years less attractive to users. According to that, we decided to explore how the lack of some opening requirements affects the decline of the Web of Data. This paper presents the Web of Data radiography, analyzing the governmental domain as a case study. The results indicate that it is necessary to strengthen the data opening process to improve resource enrichment on the Web and have better datasets. These improvements describe that open data must be public, accessible (in machine-readable formats), described (use of robust, granular metadata), reusable (made available under an open license), complete (published in primary forms), and timely (preserve the value of the data). The implementation of these characteristics would enhance the availability and reuse of datasets. Besides, organizations must understand that opening and enriching their data require a completely new approach, and they have to pay special attention and control to this project, generally by putting money, the commitment by management at all levels, and lots of time. On the contrary, given the magnitude of availability and reuse problems identified in the opening and enrichment data process, it is believed that the Web of Data model would inevitably lose the interest it aroused at the beginning if not addressed immediately by data quality, openness, and enrichment issues. Besides, its use would be restricted to a few particular niches or would even disappear altogether.
多年来，开放数据一直在改善出版平台和以消费者为导向的过程，提供更好的开放政策和透明度。尽管组织已经尝试开放他们的数据，但是通过Web of data丰富他们的资源却一直在减少。关联数据在其生命周期的不同阶段一直遭受着明显的困难，多年来对用户的吸引力越来越小。基于此，我们决定探讨一些开放要求的缺失是如何影响Web of Data的衰落的。本文介绍了数据网络放射学，并以政府领域为例进行了分析。结果表明，要提高Web资源的丰富性，获得更好的数据集，必须加强数据开放过程。这些改进描述了开放数据必须是公开的、可访问的(以机器可读的格式)、可描述的(使用健壮的、细粒度的元数据)、可重用的(在开放许可下可用)、完整的(以主要形式发布)和及时的(保留数据的价值)。这些特征的实现将增强数据集的可用性和重用性。此外，组织必须明白，开放和丰富他们的数据需要一种全新的方法，他们必须特别关注和控制这个项目，通常是通过投入资金、各级管理层的承诺和大量的时间。相反，考虑到开放和丰富数据过程中发现的可用性和重用问题的严重性，人们认为，如果不立即解决数据质量、开放性和丰富问题，数据Web模型将不可避免地失去最初引起的兴趣。此外，它的使用将被限制在少数特定的利基，甚至完全消失。
{"title":"The Relevance of Open Data Principles for the Web of Data","authors":"Jhon Francined Herrera-Cubides, Paulo Alonso Gaona-García, Carlos Enrique Montenegro-Marin, Salvador Sánchez-Alonso","doi":"10.1155/2023/4854965","DOIUrl":"https://doi.org/10.1155/2023/4854965","url":null,"abstract":"Open data has been improving both publishing platforms and the consumers-oriented process over the years, providing better openness policies and transparency. Although organizations have tried to open their data, the enrichment of their resources through the Web of Data has been decreasing. Linked data has been suffering from notable difficulties in different stages of its life cycle, becoming over the years less attractive to users. According to that, we decided to explore how the lack of some opening requirements affects the decline of the Web of Data. This paper presents the Web of Data radiography, analyzing the governmental domain as a case study. The results indicate that it is necessary to strengthen the data opening process to improve resource enrichment on the Web and have better datasets. These improvements describe that open data must be public, accessible (in machine-readable formats), described (use of robust, granular metadata), reusable (made available under an open license), complete (published in primary forms), and timely (preserve the value of the data). The implementation of these characteristics would enhance the availability and reuse of datasets. Besides, organizations must understand that opening and enriching their data require a completely new approach, and they have to pay special attention and control to this project, generally by putting money, the commitment by management at all levels, and lots of time. On the contrary, given the magnitude of availability and reuse problems identified in the opening and enrichment data process, it is believed that the Web of Data model would inevitably lose the interest it aroused at the beginning if not addressed immediately by data quality, openness, and enrichment issues. Besides, its use would be restricted to a few particular niches or would even disappear altogether.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135552547","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}
The precise estimation of the state of health (SOH) for lithium-ion batteries (LIBs) is one of the core problems for battery management systems. To address the problem that it is difficult to accurately evaluate SOH because of the LIB capacity regeneration phenomenon, this paper proposes an approach for LIB SOH estimation using isobaric energy analysis and improved long short-term memory neural network (LSTM NN). Specifically, at first, the isobaric energy curve is plotted by analyzing the battery energy variation during the constant current charging stage. Then, the mean peak value of the isobaric energy curve is extracted as a health factor to characterize the battery SOH aging. Eventually, the LIB SOH estimation model is developed using the improved LSTM NN. In this regard, the improved LSTM NN refers to the selection of the number of hidden layers and the learning rate of the LSTM NN using the particle swarm algorithm (PSO). To verify the precision of the proposed method, validation experiments are performed based on four battery aging data with different charging multipliers. The experimental results indicate that the proposed method can effectively estimate the LIB SOH. Meanwhile, the proposed method is compared with other conventional machine learning algorithms, which demonstrates that the proposed method has better estimation performance.
{"title":"Lithium-Ion Battery State-of-Health Estimation Method Using Isobaric Energy Analysis and PSO-LSTM","authors":"Shaishai Zhao, Laijin Luo, Shanhe Jiang, Chaolong Zhang","doi":"10.1155/2023/5566965","DOIUrl":"https://doi.org/10.1155/2023/5566965","url":null,"abstract":"The precise estimation of the state of health (SOH) for lithium-ion batteries (LIBs) is one of the core problems for battery management systems. To address the problem that it is difficult to accurately evaluate SOH because of the LIB capacity regeneration phenomenon, this paper proposes an approach for LIB SOH estimation using isobaric energy analysis and improved long short-term memory neural network (LSTM NN). Specifically, at first, the isobaric energy curve is plotted by analyzing the battery energy variation during the constant current charging stage. Then, the mean peak value of the isobaric energy curve is extracted as a health factor to characterize the battery SOH aging. Eventually, the LIB SOH estimation model is developed using the improved LSTM NN. In this regard, the improved LSTM NN refers to the selection of the number of hidden layers and the learning rate of the LSTM NN using the particle swarm algorithm (PSO). To verify the precision of the proposed method, validation experiments are performed based on four battery aging data with different charging multipliers. The experimental results indicate that the proposed method can effectively estimate the LIB SOH. Meanwhile, the proposed method is compared with other conventional machine learning algorithms, which demonstrates that the proposed method has better estimation performance.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135937817","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}
The management of clean energy is usually the key for environmental, economic, and sustainable developments. In the meantime, the energy management system (EMS) ensures the clean energy which includes many sources grouped in a small power plant such as microgrid (MG). In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy. The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator. At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons. Furthermore, this current review paper can support the tenders in the PV power forecasting.
{"title":"Solar Photovoltaic Power Forecasting","authors":"Abdelhakim El hendouzi, Abdennaser Bourouhou","doi":"10.1155/2020/8819925","DOIUrl":"https://doi.org/10.1155/2020/8819925","url":null,"abstract":"The management of clean energy is usually the key for environmental, economic, and sustainable developments. In the meantime, the energy management system (EMS) ensures the clean energy which includes many sources grouped in a small power plant such as microgrid (MG). In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy. The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator. At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons. Furthermore, this current review paper can support the tenders in the PV power forecasting.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"2020 1","pages":"1-21"},"PeriodicalIF":2.4,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44782534","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}
Deguang Li, Tianhao Wu, Xiaohui Li, Qiurui He, Zhanyou Cui
Environmental quality is a great concern to everyone, in order to realize the collection, upload, management, and visualization of parameters of atmospheric environment in real time. We propose a cheap, low-power, and fast deployment wireless sensor node for environmental monitoring, consisting of STM32 MCU, ESP8266, light sensor, rain sensor, UV sensor, seven-in-one sensor (including temperature, humidity, PM2.5, PM10, CO2, formaldehyde, and TVOC), and solar automatic tracking module. A customized μC/OS-III runs on the node, which controls the transmission of environment parameters collected by each sensor to the cloud server through the wireless network, and then the server receives, stores, and visualizes the data. In actual test, the node collects data once an hour, and the running power of the node is low and stable. Experimental results show that the node could achieve accurate collection and transmission and display the environmental data, and solar automatic tracking module could meet long-term running of the node in the night and continuous rainy days.
{"title":"A Wireless Multisensor Node for Long-Term Environmental Parameters Monitoring","authors":"Deguang Li, Tianhao Wu, Xiaohui Li, Qiurui He, Zhanyou Cui","doi":"10.1155/2020/8872711","DOIUrl":"https://doi.org/10.1155/2020/8872711","url":null,"abstract":"Environmental quality is a great concern to everyone, in order to realize the collection, upload, management, and visualization of parameters of atmospheric environment in real time. We propose a cheap, low-power, and fast deployment wireless sensor node for environmental monitoring, consisting of STM32 MCU, ESP8266, light sensor, rain sensor, UV sensor, seven-in-one sensor (including temperature, humidity, PM2.5, PM10, CO2, formaldehyde, and TVOC), and solar automatic tracking module. A customized μC/OS-III runs on the node, which controls the transmission of environment parameters collected by each sensor to the cloud server through the wireless network, and then the server receives, stores, and visualizes the data. In actual test, the node collects data once an hour, and the running power of the node is low and stable. Experimental results show that the node could achieve accurate collection and transmission and display the environmental data, and solar automatic tracking module could meet long-term running of the node in the night and continuous rainy days.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"2020 1","pages":"1-12"},"PeriodicalIF":2.4,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47385843","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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