Pub Date : 2023-06-01DOI: 10.23919/CJEE.2023.000020
Yongchang Zhang;Wenjia Shen;Haitao Yang
Deadbeat predictive current control (DPCC) has been widely applied in permanent magnet synchronous motor (PMSM) drives due to its fast dynamic response and good steady-state performance. However, the control accuracy of DPCC is dependent on the machine parameters' accuracy. In practical applications, the machine parameters may vary with working conditions due to temperature, saturation, skin effect, and so on. As a result, the performance of DPCC may degrade when there are parameter mismatches between the actual value and the one used in the controller. To solve the problem of parameter dependence for DPCC, this study proposes an improved model-free predictive current control method for PMSM drives. The accurate model of the PMSM is replaced by a first-order ultra-local model. This model is dynamically updated by online estimation of the gain of the input voltage and the other parts describing the system dynamics. After obtaining this ultra-local model from the information on the measured stator currents and applied stator voltages in past control periods, the reference voltage value can be calculated based on the principle of DPCC, which is subsequently synthesized by space vector modulation (SVM). This method is compared with conventional DPCC and field-oriented control (FOC), and its superiority is verified by the presented experimental results.
{"title":"An Improved Deadbeat Predictive Current Control of PMSM Drives Based on the Ultra-local Model","authors":"Yongchang Zhang;Wenjia Shen;Haitao Yang","doi":"10.23919/CJEE.2023.000020","DOIUrl":"10.23919/CJEE.2023.000020","url":null,"abstract":"Deadbeat predictive current control (DPCC) has been widely applied in permanent magnet synchronous motor (PMSM) drives due to its fast dynamic response and good steady-state performance. However, the control accuracy of DPCC is dependent on the machine parameters' accuracy. In practical applications, the machine parameters may vary with working conditions due to temperature, saturation, skin effect, and so on. As a result, the performance of DPCC may degrade when there are parameter mismatches between the actual value and the one used in the controller. To solve the problem of parameter dependence for DPCC, this study proposes an improved model-free predictive current control method for PMSM drives. The accurate model of the PMSM is replaced by a first-order ultra-local model. This model is dynamically updated by online estimation of the gain of the input voltage and the other parts describing the system dynamics. After obtaining this ultra-local model from the information on the measured stator currents and applied stator voltages in past control periods, the reference voltage value can be calculated based on the principle of DPCC, which is subsequently synthesized by space vector modulation (SVM). This method is compared with conventional DPCC and field-oriented control (FOC), and its superiority is verified by the presented experimental results.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10173479/10173643.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46923507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.23919/CJEE.2023.000022
Rashmi Patel;R. Chudamani
Multi-converter system is mainly used in advanced automotive systems. Different converters and inverters are taking part in automotive systems to provide different voltage levels in a multi-converter system. It involves constant voltage load (CVL), constant power load (CPL) and other loads. The CPL in such systems offers negative impedance characteristic and it creates a destabilizing effect on the main converter. The effect of destabilization can be reduced by increasing the CVL or inserting parasitic components. Attempts have been made by authors to improve the stability by using parasitics of different components such as switch, diode and inductor. Influence of insertion of parasitics including the series equivalent resistance of the filter capacitor and variation in CVL on the performance of main converter is mathematically analyzed and conflicting behavior between system stability and efficiency is observed. The optimum solution between these two functions is obtained by using multi-objective decision making (MODM) by varying parasitics of different components and CVL. An attempt has been made to demonstrate the effect of CVL load and the parasitics on the stability and efficiency of the main converter, experimentally.
{"title":"Stability Analysis and Efficiency Improvement of a Multi-converter System Using Multi-objective Decision Making","authors":"Rashmi Patel;R. Chudamani","doi":"10.23919/CJEE.2023.000022","DOIUrl":"10.23919/CJEE.2023.000022","url":null,"abstract":"Multi-converter system is mainly used in advanced automotive systems. Different converters and inverters are taking part in automotive systems to provide different voltage levels in a multi-converter system. It involves constant voltage load (CVL), constant power load (CPL) and other loads. The CPL in such systems offers negative impedance characteristic and it creates a destabilizing effect on the main converter. The effect of destabilization can be reduced by increasing the CVL or inserting parasitic components. Attempts have been made by authors to improve the stability by using parasitics of different components such as switch, diode and inductor. Influence of insertion of parasitics including the series equivalent resistance of the filter capacitor and variation in CVL on the performance of main converter is mathematically analyzed and conflicting behavior between system stability and efficiency is observed. The optimum solution between these two functions is obtained by using multi-objective decision making (MODM) by varying parasitics of different components and CVL. An attempt has been made to demonstrate the effect of CVL load and the parasitics on the stability and efficiency of the main converter, experimentally.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10173479/10173694.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41813310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.23919/CJEE.2023.000021
Issam A. Smadi;Luay I. Shehadeh
Accurate reactive power sharing is one of the main issues in isolated microgrids to avoid circulating currents and overloading small distributed generation (DG) units. A simple and enhanced method for improving reactive power sharing among parallel-connected DG systems in an isolated microgrid was proposed. The proposed method uses a compensator term with an integral action to minimize the reactive power-sharing error internally without any need for communication or information shared among the DG units. Moreover, a small disturbance carrying part of the reactive power-sharing error is injected into the active power-droop controller, maintaining the essential system parameters within their allowable limits. Consequently, a simple compensation trigger system is proposed to effectively detect any local load change in the network and provide compensation gains to activate the proposed control method. The stability of the proposed method was verified and analyzed using a detailed small-signal model. Moreover, the effectiveness and robustness of the proposed method were validated through comprehensive simulation studies and comparisons with other related techniques.
{"title":"An Improved Reactive Power Sharing in an Isolated Microgrid with a Local Load Detection","authors":"Issam A. Smadi;Luay I. Shehadeh","doi":"10.23919/CJEE.2023.000021","DOIUrl":"10.23919/CJEE.2023.000021","url":null,"abstract":"Accurate reactive power sharing is one of the main issues in isolated microgrids to avoid circulating currents and overloading small distributed generation (DG) units. A simple and enhanced method for improving reactive power sharing among parallel-connected DG systems in an isolated microgrid was proposed. The proposed method uses a compensator term with an integral action to minimize the reactive power-sharing error internally without any need for communication or information shared among the DG units. Moreover, a small disturbance carrying part of the reactive power-sharing error is injected into the active power-droop controller, maintaining the essential system parameters within their allowable limits. Consequently, a simple compensation trigger system is proposed to effectively detect any local load change in the network and provide compensation gains to activate the proposed control method. The stability of the proposed method was verified and analyzed using a detailed small-signal model. Moreover, the effectiveness and robustness of the proposed method were validated through comprehensive simulation studies and comparisons with other related techniques.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10173479/10173642.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41591901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.23919/CJEE.2023.000019
Jyoti Gupta;Rakesh Maurya;Sabha Raj Arya
An on-board charger for efficiently charging multiple battery-operated electric vehicles (EVs) is introduced. It has evolved as a single-input dual-output (SIDO) integrated boost-single ended primary inductor converter (SEPIC) fly-back converter, offering cost-effectiveness, reliability, and higher efficiency compared to conventional chargers with equivalent specifications. The proposed system includes an additional regulated output terminal, in addition to an existing terminal for charging the EV battery of a 4-wheeler, which can be used to charge another EV battery, preferably a 2-wheeler. With the aid of control techniques, unity power factor operations are obtained during constant-voltage (CV)/constant-current (CC) charging for the grid-to-vehicle (G2V) operating mode. Using mathematical modelling analysis, the proposed system is developed in a Matlab/Simulink environment, and the results are validated in a real-time simulator using dSPACE-1104. The proposed system is employed for charging the batteries of two EVs with capacities of 400 V, 40 A · h and 48 V, 52 A · h for the 4-wheeler and 2-wheeler, respectively. Its performance is investigated under different operating modes and over a wide range of supply voltage variations to ensure safe and reliable operation of the charger.
介绍了一种可对多辆电动汽车进行高效充电的车载充电器。它已经发展成为单输入双输出(SIDO)集成升压-单端初级电感转换器(SEPIC)反激转换器，与同等规格的传统充电器相比，具有成本效益，可靠性和更高的效率。拟议的系统包括一个额外的调节输出终端，除了一个现有的终端充电的电动汽车电池的4轮车，它可以用来充电另一个电动汽车电池，最好是2轮车。在控制技术的帮助下，电网对车辆(G2V)工作模式在恒压(CV)/恒流(CC)充电时获得了统一的功率因数运行。通过数学建模分析，在Matlab/Simulink环境下开发了该系统，并在dSPACE-1104实时仿真器上对结果进行了验证。该系统分别为容量为400 V 40 A·h和48 V 52 A·h的4轮和2轮电动汽车的电池充电。为了保证充电器的安全可靠运行，研究了其在不同工作模式和大范围电源电压变化下的性能。
{"title":"Designing an On-board Charger to Efficiently Charge Multiple Electric Vehicles","authors":"Jyoti Gupta;Rakesh Maurya;Sabha Raj Arya","doi":"10.23919/CJEE.2023.000019","DOIUrl":"10.23919/CJEE.2023.000019","url":null,"abstract":"An on-board charger for efficiently charging multiple battery-operated electric vehicles (EVs) is introduced. It has evolved as a single-input dual-output (SIDO) integrated boost-single ended primary inductor converter (SEPIC) fly-back converter, offering cost-effectiveness, reliability, and higher efficiency compared to conventional chargers with equivalent specifications. The proposed system includes an additional regulated output terminal, in addition to an existing terminal for charging the EV battery of a 4-wheeler, which can be used to charge another EV battery, preferably a 2-wheeler. With the aid of control techniques, unity power factor operations are obtained during constant-voltage (CV)/constant-current (CC) charging for the grid-to-vehicle (G2V) operating mode. Using mathematical modelling analysis, the proposed system is developed in a Matlab/Simulink environment, and the results are validated in a real-time simulator using dSPACE-1104. The proposed system is employed for charging the batteries of two EVs with capacities of 400 V, 40 A · h and 48 V, 52 A · h for the 4-wheeler and 2-wheeler, respectively. Its performance is investigated under different operating modes and over a wide range of supply voltage variations to ensure safe and reliable operation of the charger.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10173479/10173695.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41848420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.23919/CJEE.2023.000016
Yifeng Wang;Zheng Zhao;Jiangtao Li
Wearable cardiac monitoring devices can provide uninterrupted monitoring of cardiac activities over a long period of time. They have developed rapidly in recent years in terms of convenience, comfort, and intelligence. Aided by the development of sensor and materials technology, big data and artificial intelligence, wearable cardiac monitoring can become a crucial basis for novel medical models in the future. Herein, the basic concepts and representative devices of wearable cardiac monitoring are first introduced. Subsequently, its core technologies and the latest representative research progress in physiology signal sensing, signal quality enhancement, and signal reliability are systematically reviewed. Finally, an insight and outlook on the future development trends and challenges of wearable cardiac monitoring are discussed.
{"title":"Current Status and Development Tendency of Wearable Cardiac Health Monitoring","authors":"Yifeng Wang;Zheng Zhao;Jiangtao Li","doi":"10.23919/CJEE.2023.000016","DOIUrl":"10.23919/CJEE.2023.000016","url":null,"abstract":"Wearable cardiac monitoring devices can provide uninterrupted monitoring of cardiac activities over a long period of time. They have developed rapidly in recent years in terms of convenience, comfort, and intelligence. Aided by the development of sensor and materials technology, big data and artificial intelligence, wearable cardiac monitoring can become a crucial basis for novel medical models in the future. Herein, the basic concepts and representative devices of wearable cardiac monitoring are first introduced. Subsequently, its core technologies and the latest representative research progress in physiology signal sensing, signal quality enhancement, and signal reliability are systematically reviewed. Finally, an insight and outlook on the future development trends and challenges of wearable cardiac monitoring are discussed.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10093776/10093785.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44741507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.23919/CJEE.2023.000008
Yi Chang;Ming Dong;Bin Wang;Ming Ren;Lihong Fan
With the development of computer hardware and the growth of clinical database, tremendous progress has been made in the application of deep learning to electrocardiographic data, which provides new ideas for the ex vivo cardiac electrical mapping of atrial fibrillation (AF) substrates. The AF mechanism and current status of AF substrate research are first summarized. Then, the advantages and limitations of cardiac electrophysiological mapping techniques are analyzed. Finally, the application of deep learning to electrocardiogram (ECG) data is reviewed, the problems with the ex vivo intelligent labeling of an AF substrate and the possible solutions are discussed, an outlook on future development is provided.
{"title":"Review of Ex Vivo Cardiac Electrical Mapping and Intelligent Labeling of Atrial Fibrillation Substrates","authors":"Yi Chang;Ming Dong;Bin Wang;Ming Ren;Lihong Fan","doi":"10.23919/CJEE.2023.000008","DOIUrl":"10.23919/CJEE.2023.000008","url":null,"abstract":"With the development of computer hardware and the growth of clinical database, tremendous progress has been made in the application of deep learning to electrocardiographic data, which provides new ideas for the ex vivo cardiac electrical mapping of atrial fibrillation (AF) substrates. The AF mechanism and current status of AF substrate research are first summarized. Then, the advantages and limitations of cardiac electrophysiological mapping techniques are analyzed. Finally, the application of deep learning to electrocardiogram (ECG) data is reviewed, the problems with the ex vivo intelligent labeling of an AF substrate and the possible solutions are discussed, an outlook on future development is provided.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10093776/10093808.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48780201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.23919/CJEE.2023.000004
Jun Li;Qibo Liu
The inherent intermittency and uncertainty of photovoltaic (PV) power generation impede the development of grid-connected PV systems. Accurately forecasting PV output power is an effective way to address this problem. A hybrid forecasting model that combines the clustering of a trained self-organizing map (SOM) network and an optimized kernel extreme learning machine (KELM) method to improve the accuracy of short-term PV power generation forecasting are proposed. First, pure SOM is employed to complete the initial partitions of the training dataset; then the fuzzy c-means (FCM) algorithm is used to cluster the trained SOM network and the Davies-Bouldin index (DBI) is utilized to determine the optimal size of clusters, simultaneously. Finally, in each data partition, the clusters are combined with the KELM method optimized by differential evolution algorithm to establish a regional KELM model or combined with multiple linear regression (MR) using least squares to complete coefficient evaluation to establish a regional MR model. The proposed models are applied to one-hour-ahead PV power forecasting instances in three different solar power plants provided by GEFCom2014. Compared with other single global models, the root mean square errors (RMSEs) of the proposed regional KELM model are reduced by 52.06% in plant 1, 54.56% in plant 2, and 51.43% in plant 3 on average. Such results demonstrate that the forecasting accuracy has been significantly improved using the proposed models. In addition, the comparisons between the proposed and existing state-of-the-art forecasting methods presented have demonstrated the superiority of the proposed methods. The forecasts of different methods in different seasons revealed the strong robustness of the proposed method. In four seasons, the MAEs and RMSEs of the proposed SF-KELM are generally the smallest. Moreover, the $R^{2}$ value exceeds 0.9, which is the closest to 1.
{"title":"Short-term Photovoltaic Power Forecasting Using SOM-based Regional Modelling Methods","authors":"Jun Li;Qibo Liu","doi":"10.23919/CJEE.2023.000004","DOIUrl":"10.23919/CJEE.2023.000004","url":null,"abstract":"The inherent intermittency and uncertainty of photovoltaic (PV) power generation impede the development of grid-connected PV systems. Accurately forecasting PV output power is an effective way to address this problem. A hybrid forecasting model that combines the clustering of a trained self-organizing map (SOM) network and an optimized kernel extreme learning machine (KELM) method to improve the accuracy of short-term PV power generation forecasting are proposed. First, pure SOM is employed to complete the initial partitions of the training dataset; then the fuzzy c-means (FCM) algorithm is used to cluster the trained SOM network and the Davies-Bouldin index (DBI) is utilized to determine the optimal size of clusters, simultaneously. Finally, in each data partition, the clusters are combined with the KELM method optimized by differential evolution algorithm to establish a regional KELM model or combined with multiple linear regression (MR) using least squares to complete coefficient evaluation to establish a regional MR model. The proposed models are applied to one-hour-ahead PV power forecasting instances in three different solar power plants provided by GEFCom2014. Compared with other single global models, the root mean square errors (RMSEs) of the proposed regional KELM model are reduced by 52.06% in plant 1, 54.56% in plant 2, and 51.43% in plant 3 on average. Such results demonstrate that the forecasting accuracy has been significantly improved using the proposed models. In addition, the comparisons between the proposed and existing state-of-the-art forecasting methods presented have demonstrated the superiority of the proposed methods. The forecasts of different methods in different seasons revealed the strong robustness of the proposed method. In four seasons, the MAEs and RMSEs of the proposed SF-KELM are generally the smallest. Moreover, the \u0000<tex>$R^{2}$</tex>\u0000 value exceeds 0.9, which is the closest to 1.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10093776/10093784.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47396863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.23919/cjee.2023.000001
Yunhao Yang, Heng Zhang, Shurong Peng, Sheng Su, Bin Li
{"title":"Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network","authors":"Yunhao Yang, Heng Zhang, Shurong Peng, Sheng Su, Bin Li","doi":"10.23919/cjee.2023.000001","DOIUrl":"https://doi.org/10.23919/cjee.2023.000001","url":null,"abstract":"","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68733212","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}
Invasive techniques are becoming increasingly important in the presurgical evaluation of epilepsy. Adopting the electrophysiological source imaging (ESI) of interictal scalp electroencephalography (EEG) to localize the epileptogenic zone remains a challenge. The accuracy of the preoperative localization of the epileptogenic zone is key to curing epilepsy. The T1 MRI and the boundary element method were used to build the realistic head model. To solve the inverse problem, the distributed inverse solution and equivalent current dipole (ECD) methods were employed to locate the epileptogenic zone. Furthermore, a combination of inverse solution algorithms and Granger causality connectivity measures was evaluated. The ECD method exhibited excellent focalization in lateralization and localization, achieving a coincidence rate of 99.02% ( $p < 0.05$ ) with the stereo electroencephalogram. The combination of ECD and the directed transfer function led to excellent matching between the information flow obtained from intracranial and scalp EEG recordings. The ECD inverse solution method showed the highest performance and could extract the discharge information at the cortex level from noninvasive low-density EEG data. Thus, the accurate preoperative localization of the epileptogenic zone could reduce the number of intracranial electrode implantations required.
{"title":"Interictal Electrophysiological Source Imaging Based on Realistic Epilepsy Head Model in Presurgical Evaluation: A Prospective Study","authors":"Ruowei Qu;Zhaonan Wang;Shifeng Wang;Le Wang;Alan Wang;Guizhi Xu","doi":"10.23919/CJEE.2023.000012","DOIUrl":"10.23919/CJEE.2023.000012","url":null,"abstract":"Invasive techniques are becoming increasingly important in the presurgical evaluation of epilepsy. Adopting the electrophysiological source imaging (ESI) of interictal scalp electroencephalography (EEG) to localize the epileptogenic zone remains a challenge. The accuracy of the preoperative localization of the epileptogenic zone is key to curing epilepsy. The T1 MRI and the boundary element method were used to build the realistic head model. To solve the inverse problem, the distributed inverse solution and equivalent current dipole (ECD) methods were employed to locate the epileptogenic zone. Furthermore, a combination of inverse solution algorithms and Granger causality connectivity measures was evaluated. The ECD method exhibited excellent focalization in lateralization and localization, achieving a coincidence rate of 99.02% (\u0000<tex>$p < 0.05$</tex>\u0000) with the stereo electroencephalogram. The combination of ECD and the directed transfer function led to excellent matching between the information flow obtained from intracranial and scalp EEG recordings. The ECD inverse solution method showed the highest performance and could extract the discharge information at the cortex level from noninvasive low-density EEG data. Thus, the accurate preoperative localization of the epileptogenic zone could reduce the number of intracranial electrode implantations required.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10093776/10093783.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48470531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.23919/CJEE.2023.000001
Yunhao Yang;Heng Zhang;Shurong Peng;Sheng Su;Bin Li
Aiming at the wind power prediction problem, a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed. With the developed model, the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours. The presented method can obtain more useful information than conventional point and interval predictions. Moreover, a prediction of the future complete probability distribution of wind power can be realized. According to the actual data forecast of wind power in the PJM network in the United States, the proposed probability density prediction approach can not only obtain more accurate point prediction results, it also obtains the complete probability density curve prediction results for wind power. Compared with two other quantile regression methods, the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.
{"title":"Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network","authors":"Yunhao Yang;Heng Zhang;Shurong Peng;Sheng Su;Bin Li","doi":"10.23919/CJEE.2023.000001","DOIUrl":"https://doi.org/10.23919/CJEE.2023.000001","url":null,"abstract":"Aiming at the wind power prediction problem, a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed. With the developed model, the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours. The presented method can obtain more useful information than conventional point and interval predictions. Moreover, a prediction of the future complete probability distribution of wind power can be realized. According to the actual data forecast of wind power in the PJM network in the United States, the proposed probability density prediction approach can not only obtain more accurate point prediction results, it also obtains the complete probability density curve prediction results for wind power. Compared with two other quantile regression methods, the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7873788/10093776/10093777.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49939763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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