Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.00440
Baoluo Li;Shiyun Xu;Huadong Sun;Zonghan Li;Lin Yu
Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.
{"title":"System Strength Assessment Based on Multi-task Learning","authors":"Baoluo Li;Shiyun Xu;Huadong Sun;Zonghan Li;Lin Yu","doi":"10.17775/CSEEJPES.2023.00440","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00440","url":null,"abstract":"Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"41-50"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.00190
Tianyun Zhang;Jun Zhang;Feiyue Wang;Peidong Xu;Tianlu Gao;Haoran Zhang;Ruiqi Si
In artificial intelligence (AI) based-complex power system management and control technology, one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution. However, there is, currently, nearly no standard technical framework for objective and quantitative intelligence evaluation. In this article, based on a parallel system framework, a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems, by resorting to human intelligence evaluation theories. On this basis, this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning (AutoRL) systems. A parallel system based quantitative assessment and self-evolution (PLASE) system for power grid corrective control AI is thereby constructed, taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results. Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent, and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results, effectively, as well as intuitively improving its intelligence level through self-evolution.
{"title":"Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control","authors":"Tianyun Zhang;Jun Zhang;Feiyue Wang;Peidong Xu;Tianlu Gao;Haoran Zhang;Ruiqi Si","doi":"10.17775/CSEEJPES.2023.00190","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00190","url":null,"abstract":"In artificial intelligence (AI) based-complex power system management and control technology, one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution. However, there is, currently, nearly no standard technical framework for objective and quantitative intelligence evaluation. In this article, based on a parallel system framework, a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems, by resorting to human intelligence evaluation theories. On this basis, this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning (AutoRL) systems. A parallel system based quantitative assessment and self-evolution (PLASE) system for power grid corrective control AI is thereby constructed, taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results. Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent, and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results, effectively, as well as intuitively improving its intelligence level through self-evolution.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"13-28"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.05240
Boyuan Yin;Xianwu Zeng;John Frederick Eastham;Emelie Nilsson;Jean-francois Rouquette;Jean Rivenc;Ludovic Ybanez;Xiaoze Pei
Hydrogen-powered electric aircraft have attracted significant interests aiming to achieve decarbonization targets. Onboard DC electric networks are facing great challenges in DC fault protection requirements. Vacuum interrupters are widely used in low voltage and medium voltage power systems due to being environmentally friendly with low maintenance. In this paper a moving coil actuator with compensation coils for a vacuum interrupter, as part of a hybrid direct current circuit breaker, is designed and experimentally tested. Compensation coils are used to improve operating speed compared with original moving coil actuator. Comparisons between four possible connections of compensation coils and original moving coil actuator are carried out. Experimental results show comparisons between different connections of actuator coils in terms of opening time and coil current with a range of pre-charged capacitor voltages. Dynamic performance of each actuator connection is also compared. The actuator with compensation coils is shown to have a higher current rising rate and achieve faster opening speed, which is a critical requirement for electric aircraft network protection. The parallel connection actuator achieves the highest opening speed within 3.5 ms with capacitor voltage of 50 V.
氢动力电动飞机在实现脱碳目标方面备受关注。机载直流电网在直流故障保护要求方面面临巨大挑战。真空灭弧室因其环保和低维护成本的特点，被广泛应用于低压和中压电力系统。本文设计了一种带补偿线圈的动圈传动装置，用于真空灭弧室，作为混合直流断路器的一部分，并进行了实验测试。与原来的动圈传动器相比，补偿线圈用于提高运行速度。对补偿线圈的四种可能连接方式和原始动圈推杆进行了比较。实验结果表明，在预充电容电压范围内，不同连接方式的致动器线圈在打开时间和线圈电流方面都有可比性。此外，还比较了每种致动器连接的动态性能。结果表明，带补偿线圈的致动器具有更高的电流上升率和更快的打开速度，而这正是飞机电网保护的关键要求。并联致动器在电容器电压为 50 V 时，可在 3.5 ms 内达到最高打开速度。
{"title":"Design and Experimental Testing of a Moving Coil Actuator with Compensation Coils","authors":"Boyuan Yin;Xianwu Zeng;John Frederick Eastham;Emelie Nilsson;Jean-francois Rouquette;Jean Rivenc;Ludovic Ybanez;Xiaoze Pei","doi":"10.17775/CSEEJPES.2023.05240","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05240","url":null,"abstract":"Hydrogen-powered electric aircraft have attracted significant interests aiming to achieve decarbonization targets. Onboard DC electric networks are facing great challenges in DC fault protection requirements. Vacuum interrupters are widely used in low voltage and medium voltage power systems due to being environmentally friendly with low maintenance. In this paper a moving coil actuator with compensation coils for a vacuum interrupter, as part of a hybrid direct current circuit breaker, is designed and experimentally tested. Compensation coils are used to improve operating speed compared with original moving coil actuator. Comparisons between four possible connections of compensation coils and original moving coil actuator are carried out. Experimental results show comparisons between different connections of actuator coils in terms of opening time and coil current with a range of pre-charged capacitor voltages. Dynamic performance of each actuator connection is also compared. The actuator with compensation coils is shown to have a higher current rising rate and achieve faster opening speed, which is a critical requirement for electric aircraft network protection. The parallel connection actuator achieves the highest opening speed within 3.5 ms with capacitor voltage of 50 V.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 2","pages":"707-716"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation, online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges, this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First, a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted, which takes into account accuracy and rapidity at the same time. Next, a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen, optimal emergency control strategy can be determined in time. Lastly, responses after emergency control are fed back to the TSP model. If prediction result is still unstable, an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.
{"title":"Adaptive Emergency Control of Power Systems Based on Deep Belief Network","authors":"Junyong Wu;Baoqin Li;Liangliang Hao;Fashun Shi;Pengjie Zhao","doi":"10.17775/CSEEJPES.2022.00070","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.00070","url":null,"abstract":"Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation, online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges, this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First, a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted, which takes into account accuracy and rapidity at the same time. Next, a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen, optimal emergency control strategy can be determined in time. Lastly, responses after emergency control are fed back to the TSP model. If prediction result is still unstable, an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 4","pages":"1618-1631"},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375981","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.
{"title":"Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control","authors":"Shixiong Fan;Jianbo Guo;Shicong Ma;Lixin Li;Guozheng Wang;Haotian Xu;Jin Yang;Zening Zhao","doi":"10.17775/CSEEJPES.2023.00940","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00940","url":null,"abstract":"With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"1-12"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.01280
Zexin Zhao;Weijiang Chen;Zhichang Yang;Guoliang Zhao;Bin Han;Yunfei Xu;Nianwen Xiang;Shulai Wang
The modular multilevel matrix converter (M3C) is a potential frequency converter for low-frequency AC transmission. However, capacitor voltage control of high-voltage and large-capacity M3C is more difficult, especially for voltage balancing between branches. To solve this problem, this paper defines sequence circulating components and theoretically analyzes the influence mechanism of different sequence circulating components on branch capacitor voltage. A fully decoupled branch energy balancing control method based on four groups of sequence circulating components is proposed. This method can control capacitor voltages of nine branches in horizontal, vertical and diagonal directions. Considering influences of both circulating current and voltage, a cross decoupled control is designed to improve control precision. Simulation results are taken from a low-frequency transmission system based on PSCAD/EMTDC, and effectiveness and precision of the proposed branch energy balancing control method are verified in the case of nonuniform parameters and an unbalanced power system.
{"title":"Fully Decoupled Branch Energy Balancing Control Method for Modular Multilevel Matrix Converter Based on Sequence Circulating Components","authors":"Zexin Zhao;Weijiang Chen;Zhichang Yang;Guoliang Zhao;Bin Han;Yunfei Xu;Nianwen Xiang;Shulai Wang","doi":"10.17775/CSEEJPES.2023.01280","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.01280","url":null,"abstract":"The modular multilevel matrix converter (M3C) is a potential frequency converter for low-frequency AC transmission. However, capacitor voltage control of high-voltage and large-capacity M3C is more difficult, especially for voltage balancing between branches. To solve this problem, this paper defines sequence circulating components and theoretically analyzes the influence mechanism of different sequence circulating components on branch capacitor voltage. A fully decoupled branch energy balancing control method based on four groups of sequence circulating components is proposed. This method can control capacitor voltages of nine branches in horizontal, vertical and diagonal directions. Considering influences of both circulating current and voltage, a cross decoupled control is designed to improve control precision. Simulation results are taken from a low-frequency transmission system based on PSCAD/EMTDC, and effectiveness and precision of the proposed branch energy balancing control method are verified in the case of nonuniform parameters and an unbalanced power system.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"235-247"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.05970
Yonghua Song;Ge Chen;Hongcai Zhang
Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks (ADNs) to facilitate integration of distributed renewable generation. Due to unavailability of network topology and line impedance in many distribution networks, physical model-based methods may not be applicable to their operations. To tackle this challenge, some studies have proposed constraint learning, which replicates physical models by training a neural network to evaluate feasibility of a decision (i.e., whether a decision satisfies all critical constraints or not). To ensure accuracy of this trained neural network, training set should contain sufficient feasible and infeasible samples. However, since ADNs are mostly operated in a normal status, only very few historical samples are infeasible. Thus, the historical dataset is highly imbalanced, which poses a significant obstacle to neural network training. To address this issue, we propose an enhanced constraint learning method. First, it leverages constraint learning to train a neural network as surrogate of ADN's model. Then, it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset. By incorporating historical and synthetic samples into the training set, we can significantly improve accuracy of neural network. Furthermore, we establish a trust region to constrain and thereafter enhance reliability of the solution. Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.
{"title":"Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data","authors":"Yonghua Song;Ge Chen;Hongcai Zhang","doi":"10.17775/CSEEJPES.2023.05970","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05970","url":null,"abstract":"Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks (ADNs) to facilitate integration of distributed renewable generation. Due to unavailability of network topology and line impedance in many distribution networks, physical model-based methods may not be applicable to their operations. To tackle this challenge, some studies have proposed constraint learning, which replicates physical models by training a neural network to evaluate feasibility of a decision (i.e., whether a decision satisfies all critical constraints or not). To ensure accuracy of this trained neural network, training set should contain sufficient feasible and infeasible samples. However, since ADNs are mostly operated in a normal status, only very few historical samples are infeasible. Thus, the historical dataset is highly imbalanced, which poses a significant obstacle to neural network training. To address this issue, we propose an enhanced constraint learning method. First, it leverages constraint learning to train a neural network as surrogate of ADN's model. Then, it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset. By incorporating historical and synthetic samples into the training set, we can significantly improve accuracy of neural network. Furthermore, we establish a trust region to constrain and thereafter enhance reliability of the solution. Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"51-65"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.05850
Jinliang He;Zhifei Han;Jun Hu
The ubiquitous power Internet of Things (UPIoT) uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system. UPIoT has the characteristics of comprehensive state perception and efficient information processing, and has broad application prospects for transformation of the energy industry. The fundamental facility of the UPIoT is the sensor-based information network. By using advanced sensors, Wireless Sensor Networks (WSNs), and advanced data processing technologies, Internet of Things can be realized in the power system. In this paper, a framework of WSNs based on advanced sensors towards UPIoT is proposed. In addition, the most advanced sensors for UPIoT purposes are reviewed, along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.
{"title":"Advanced Sensors Towards Ubiquitous Power Internet of Things","authors":"Jinliang He;Zhifei Han;Jun Hu","doi":"10.17775/CSEEJPES.2023.05850","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05850","url":null,"abstract":"The ubiquitous power Internet of Things (UPIoT) uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system. UPIoT has the characteristics of comprehensive state perception and efficient information processing, and has broad application prospects for transformation of the energy industry. The fundamental facility of the UPIoT is the sensor-based information network. By using advanced sensors, Wireless Sensor Networks (WSNs), and advanced data processing technologies, Internet of Things can be realized in the power system. In this paper, a framework of WSNs based on advanced sensors towards UPIoT is proposed. In addition, the most advanced sensors for UPIoT purposes are reviewed, along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 3","pages":"871-890"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SF 6 degradation mechanism in non-thermal plasma (NTP) systems is not fully understood due to the formation of a complex physico-chemical reaction network, especially when reactive gases and packing materials are involved. In this work, we conduct a combined experimental and theoretical study to unravel the SF 6 degradation path in a γ-Al 2 O 3 packed plasma in the presence of H 2 O or O 2 . Our experimental results show that both H 2 O and O 2 have a synergetic effect with γ-A1 2 O 3 packing on promoting SF 6 degradation, leading to higher stable gas yields than typical spark or corona discharges. HO or O 2 addition promotes SO 2 or SO 2 F 2 selectivity, respectively. Density functional theory (DFT) calculations reveal that SO 2 generation corresponding with the highest activation barrier is the most critical step toward SF 6 degradation. Radicals like H and O generated from H 2 O or O 2 discharge can significantly promote the degradation process via Eley-Rideal mechanism, affecting key reactions of stable product generation, advancing degradation efficiency. The results of this work could provide insights on further understanding SF 6 degradation mechanism especially in packed-bed plasma systems.
{"title":"Investigation on Degradation Path of SF6 in Packed-Bed Plasma: Effect of Plasma-generated Radicals","authors":"Zhaolun Cui;Chang Zhou;Amin Jafarzadeh;Xiaoxing Zhang;Peng Gao;Licheng Li;Yanpeng Hao","doi":"10.17775/CSEEJPES.2022.05910","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.05910","url":null,"abstract":"SF\u0000<inf>6</inf>\u0000degradation mechanism in non-thermal plasma (NTP) systems is not fully understood due to the formation of a complex physico-chemical reaction network, especially when reactive gases and packing materials are involved. In this work, we conduct a combined experimental and theoretical study to unravel the SF\u0000<inf>6</inf>\u0000 degradation path in a γ-Al\u0000<inf>2</inf>\u0000O\u0000<inf>3</inf>\u0000packed plasma in the presence of H\u0000<inf>2</inf>\u0000O or O\u0000<inf>2</inf>\u0000. Our experimental results show that both H\u0000<inf>2</inf>\u0000O and O\u0000<inf>2</inf>\u0000 have a synergetic effect with γ-A1\u0000<inf>2</inf>\u0000O\u0000<inf>3</inf>\u0000 packing on promoting SF\u0000<inf>6</inf>\u0000 degradation, leading to higher stable gas yields than typical spark or corona discharges. HO or O\u0000<inf>2</inf>\u0000addition promotes SO\u0000<inf>2</inf>\u0000or SO\u0000<inf>2</inf>\u0000F\u0000<inf>2</inf>\u0000 selectivity, respectively. Density functional theory (DFT) calculations reveal that SO\u0000<inf>2</inf>\u0000 generation corresponding with the highest activation barrier is the most critical step toward SF\u0000<inf>6</inf>\u0000 degradation. Radicals like H and O generated from H\u0000<inf>2</inf>\u0000O or O\u0000<inf>2</inf>\u0000 discharge can significantly promote the degradation process via Eley-Rideal mechanism, affecting key reactions of stable product generation, advancing degradation efficiency. The results of this work could provide insights on further understanding SF\u0000<inf>6</inf>\u0000 degradation mechanism especially in packed-bed plasma systems.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 3","pages":"1231-1241"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375998","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The complexity and uncertainty in power systems cause great challenges to controlling power grids. As a popular data-driven technique, deep reinforcement learning (DRL) attracts attention in the control of power grids. However, DRL has some inherent drawbacks in terms of data efficiency and explainability. This paper presents a novel hierarchical task planning (HTP) approach, bridging planning and DRL, to the task of power line flow regulation. First, we introduce a three-level task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes (TP-MDPs). Second, we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units. In addition, we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP. Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization, a state-of-the-art deep reinforcement learning (DRL) approach, improving efficiency by 26.16% and 6.86% on both systems.
{"title":"Hierarchical Task Planning for Power Line Flow Regulation","authors":"Chenxi Wang;Youtian Du;Yanhao Huang;Yuanlin Chang;Zihao Guo","doi":"10.17775/CSEEJPES.2023.00620","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00620","url":null,"abstract":"The complexity and uncertainty in power systems cause great challenges to controlling power grids. As a popular data-driven technique, deep reinforcement learning (DRL) attracts attention in the control of power grids. However, DRL has some inherent drawbacks in terms of data efficiency and explainability. This paper presents a novel hierarchical task planning (HTP) approach, bridging planning and DRL, to the task of power line flow regulation. First, we introduce a three-level task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes (TP-MDPs). Second, we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units. In addition, we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP. Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization, a state-of-the-art deep reinforcement learning (DRL) approach, improving efficiency by 26.16% and 6.86% on both systems.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"29-40"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：