Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Powe...Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.展开更多
As a practical solution that could reduce the communication and computation load of central servers in digital factories,edge computing has been widely used in modern industry.In mobile edge computing,a reasonable off...As a practical solution that could reduce the communication and computation load of central servers in digital factories,edge computing has been widely used in modern industry.In mobile edge computing,a reasonable offloading strategy can balance the computation load and reduce the energy consumption of mobile devices,which is the key to optimizing network operation.In this paper,a Relative Position-based Bacterial Foraging Optimization algorithm with Dropout strategy,RPBFO-D,is proposed to optimize the computation offloading problem.A many-to-many relationship model of devices-tasks-servers is established,comprehensively considering the time delay and energy consumption,and RPBFO-D is proposed to solve the problem.In this algorithm,the structure and operators of the original BFO are redesigned,and the dropout strategy of the neural network maintains diversity.Experiments with parameter settings demonstrate the effectiveness of the dropout strategy.Results show that RPBFO-D has better convergence accuracy than comparison algorithms,which demonstrates that it is a competitive approach for computation offloading.展开更多
文摘Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.
基金supported in part by the Guangdong Basic and Applied Basic Research Foundation(Nos.2022A1515140093,2022A1515140035,2022A1515110924,and 2022A1515110501)the National Natural Science Foundation of China(Nos.72471060,52305550,and 52305097)+1 种基金the Guangdong S&T Program(No.2022B0303010001)the Dongguan Sci-tech Commissioner Program(No.20231800500112).
文摘As a practical solution that could reduce the communication and computation load of central servers in digital factories,edge computing has been widely used in modern industry.In mobile edge computing,a reasonable offloading strategy can balance the computation load and reduce the energy consumption of mobile devices,which is the key to optimizing network operation.In this paper,a Relative Position-based Bacterial Foraging Optimization algorithm with Dropout strategy,RPBFO-D,is proposed to optimize the computation offloading problem.A many-to-many relationship model of devices-tasks-servers is established,comprehensively considering the time delay and energy consumption,and RPBFO-D is proposed to solve the problem.In this algorithm,the structure and operators of the original BFO are redesigned,and the dropout strategy of the neural network maintains diversity.Experiments with parameter settings demonstrate the effectiveness of the dropout strategy.Results show that RPBFO-D has better convergence accuracy than comparison algorithms,which demonstrates that it is a competitive approach for computation offloading.
