Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the developme...Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.展开更多
In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low co...In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low cost in a short time.Manufacturing companies need traceability,which means a real-time view into thenproduction processes and operations.Radio frequency identification（RFID） technology enables manufacturing companies to gain instant traceability and visibility because it handles manufactured goods,materials and processes transparently.RFID has become an important driver in manufacturing and supply chain activities.However,there is still a challenge in effectively deploying RFID in manufacturing.This paper describes the importance for Norwegian manufacturing companies to implement RFID technology,and shows how the intelligent and integrated RFID（n-RFID） system,which has been developed in the Knowledge Discovery Laboratory of Norwegian University of Science and Technology,provides instant traceability and visibility into manufacturing processes.It supports the Norwegian manufacturing industries survive and thrive in global competition.The future research work will focus on the field of RFID data mining to support decision-making process in manufacturing.展开更多
基金supported by Science and Technology Project of State Grid Corporation of ChinaNational Natural Science Foundation of China (No.51777104)China State Key Laboratory of Power System (No.SKLD16Z08)
文摘Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.
文摘In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low cost in a short time.Manufacturing companies need traceability,which means a real-time view into thenproduction processes and operations.Radio frequency identification（RFID） technology enables manufacturing companies to gain instant traceability and visibility because it handles manufactured goods,materials and processes transparently.RFID has become an important driver in manufacturing and supply chain activities.However,there is still a challenge in effectively deploying RFID in manufacturing.This paper describes the importance for Norwegian manufacturing companies to implement RFID technology,and shows how the intelligent and integrated RFID（n-RFID） system,which has been developed in the Knowledge Discovery Laboratory of Norwegian University of Science and Technology,provides instant traceability and visibility into manufacturing processes.It supports the Norwegian manufacturing industries survive and thrive in global competition.The future research work will focus on the field of RFID data mining to support decision-making process in manufacturing.
