The Experts below are selected from a list of 103230 Experts worldwide ranked by ideXlab platform
Shuhong Wang - One of the best experts on this subject based on the ideXlab platform.
-
system level design optimization method for electrical drive systems robust Approach
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Gang Lei, Youguang Guo, Tianshi Wang, Jianguo Zhu, Shuhong WangAbstract:A system-level design optimization method under the framework of a Deterministic Approach was presented for electrical drive systems in our previous work, in which not only motors but also the integrated control schemes were designed and optimized to achieve good steady and dynamic performances. However, there are many unavoidable uncertainties (noise factors) in the industrial manufacturing process, such as material characteristics and manufacturing precision. These will result in big fluctuations for the product's reliability and quality in mass production, which are not investigated in the Deterministic Approach. Therefore, a robust Approach based on the technique of design for six sigma is presented for the system-level design optimization of drive systems to improve the reliability and quality of products in batch production in this work. Meanwhile, two system-level optimization frameworks are presented for the proposed method, namely, single-level (only at the system level) and multilevel frameworks. Finally, a drive system is investigated as an example, and detailed results are presented and discussed. It can be found that the reliability and quality levels of the investigated drive system have been greatly increased by using the proposed robust Approach.
-
system level design optimization methods for electrical drive systems Deterministic Approach
IEEE Transactions on Industrial Electronics, 2014Co-Authors: Tianshi Wang, Shuhong WangAbstract:Electrical drive systems are key components in modern appliances, industry equipment, and systems, e.g., hybrid electric vehicles. To obtain the best performance of these drive systems, the motors and their control systems should be designed and optimized at the system level rather than the component level. This paper presents an effort to develop system-level design and optimization methods for electrical drive systems. Two system-level design optimization methods are presented in this paper: 1) single-level method (only at system level); and 2) multilevel method. Meanwhile, the approximate models, the design of experiments technique, and the sequential subspace optimization method are presented to improve the optimization efficiency. Finally, a drive system consisting of a permanent-magnet transverse flux machine with a soft magnetic composite core is investigated, and detailed results are presented and discussed. This is a high-dimensional optimization problem with 14 parameters mixed with both discrete and continuous variables. The finite-element analysis model and method are verified by the experimental results on the motor prototype. From the discussion, it can be found that the proposed multilevel method can increase the performance of the whole drive system, such as bigger output power and lower material cost, and decrease the computation cost significantly compared with those of single-level design optimization method.
Tianshi Wang - One of the best experts on this subject based on the ideXlab platform.
-
system level design optimization method for electrical drive systems robust Approach
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Gang Lei, Youguang Guo, Tianshi Wang, Jianguo Zhu, Shuhong WangAbstract:A system-level design optimization method under the framework of a Deterministic Approach was presented for electrical drive systems in our previous work, in which not only motors but also the integrated control schemes were designed and optimized to achieve good steady and dynamic performances. However, there are many unavoidable uncertainties (noise factors) in the industrial manufacturing process, such as material characteristics and manufacturing precision. These will result in big fluctuations for the product's reliability and quality in mass production, which are not investigated in the Deterministic Approach. Therefore, a robust Approach based on the technique of design for six sigma is presented for the system-level design optimization of drive systems to improve the reliability and quality of products in batch production in this work. Meanwhile, two system-level optimization frameworks are presented for the proposed method, namely, single-level (only at the system level) and multilevel frameworks. Finally, a drive system is investigated as an example, and detailed results are presented and discussed. It can be found that the reliability and quality levels of the investigated drive system have been greatly increased by using the proposed robust Approach.
-
system level design optimization methods for electrical drive systems Deterministic Approach
IEEE Transactions on Industrial Electronics, 2014Co-Authors: Tianshi Wang, Shuhong WangAbstract:Electrical drive systems are key components in modern appliances, industry equipment, and systems, e.g., hybrid electric vehicles. To obtain the best performance of these drive systems, the motors and their control systems should be designed and optimized at the system level rather than the component level. This paper presents an effort to develop system-level design and optimization methods for electrical drive systems. Two system-level design optimization methods are presented in this paper: 1) single-level method (only at system level); and 2) multilevel method. Meanwhile, the approximate models, the design of experiments technique, and the sequential subspace optimization method are presented to improve the optimization efficiency. Finally, a drive system consisting of a permanent-magnet transverse flux machine with a soft magnetic composite core is investigated, and detailed results are presented and discussed. This is a high-dimensional optimization problem with 14 parameters mixed with both discrete and continuous variables. The finite-element analysis model and method are verified by the experimental results on the motor prototype. From the discussion, it can be found that the proposed multilevel method can increase the performance of the whole drive system, such as bigger output power and lower material cost, and decrease the computation cost significantly compared with those of single-level design optimization method.
Ram Rajagopal - One of the best experts on this subject based on the ideXlab platform.
-
probabilistic baseline estimation based on load patterns for better residential customer rewards
International Journal of Electrical Power & Energy Systems, 2018Co-Authors: Yang Weng, Jiafan Yu, Ram RajagopalAbstract:Abstract Residential customers are increasingly participating in demand response program for both economic savings and environmental benefits. For example, baseline estimation-based rewarding mechanism is currently being deployed to encourage customer participation. However, the Deterministic baseline estimation method good for commercial users was found to create erroneous rewards for residential consumers. This is due to larger uncertainty associated with residential customers and the inability of a Deterministic Approach to capturing such uncertainty. Different than the Deterministic Approach, we propose to conduct probabilistic baseline estimation and pay a customer over a period of time when the customer’s predicted error decreases due to reward aggregation. To achieve this goal, we analyze 12,000 residential customers’ data from PG&E and propose a Gaussian Process-based rewarding mechanism. Real data from PG&E and OhmConnect are used in validating the algorithm and showing fairer payment to residential customers. Finally, we provide a theoretical foundation that the proposed method is always better than the currently used industrial Approaches.
-
probabilistic baseline estimation based on load patterns for better residential customer rewards
International Journal of Electrical Power & Energy Systems, 2018Co-Authors: Yang Weng, Jiafan Yu, Ram RajagopalAbstract:Abstract Residential customers are increasingly participating in demand response program for both economic savings and environmental benefits. For example, baseline estimation-based rewarding mechanism is currently being deployed to encourage customer participation. However, the Deterministic baseline estimation method good for commercial users was found to create erroneous rewards for residential consumers. This is due to larger uncertainty associated with residential customers and the inability of a Deterministic Approach to capturing such uncertainty. Different than the Deterministic Approach, we propose to conduct probabilistic baseline estimation and pay a customer over a period of time when the customer’s predicted error decreases due to reward aggregation. To achieve this goal, we analyze 12,000 residential customers’ data from PG&E and propose a Gaussian Process-based rewarding mechanism. Real data from PG&E and OhmConnect are used in validating the algorithm and showing fairer payment to residential customers. Finally, we provide a theoretical foundation that the proposed method is always better than the currently used industrial Approaches.
Yang Weng - One of the best experts on this subject based on the ideXlab platform.
-
probabilistic baseline estimation based on load patterns for better residential customer rewards
International Journal of Electrical Power & Energy Systems, 2018Co-Authors: Yang Weng, Jiafan Yu, Ram RajagopalAbstract:Abstract Residential customers are increasingly participating in demand response program for both economic savings and environmental benefits. For example, baseline estimation-based rewarding mechanism is currently being deployed to encourage customer participation. However, the Deterministic baseline estimation method good for commercial users was found to create erroneous rewards for residential consumers. This is due to larger uncertainty associated with residential customers and the inability of a Deterministic Approach to capturing such uncertainty. Different than the Deterministic Approach, we propose to conduct probabilistic baseline estimation and pay a customer over a period of time when the customer’s predicted error decreases due to reward aggregation. To achieve this goal, we analyze 12,000 residential customers’ data from PG&E and propose a Gaussian Process-based rewarding mechanism. Real data from PG&E and OhmConnect are used in validating the algorithm and showing fairer payment to residential customers. Finally, we provide a theoretical foundation that the proposed method is always better than the currently used industrial Approaches.
-
probabilistic baseline estimation based on load patterns for better residential customer rewards
International Journal of Electrical Power & Energy Systems, 2018Co-Authors: Yang Weng, Jiafan Yu, Ram RajagopalAbstract:Abstract Residential customers are increasingly participating in demand response program for both economic savings and environmental benefits. For example, baseline estimation-based rewarding mechanism is currently being deployed to encourage customer participation. However, the Deterministic baseline estimation method good for commercial users was found to create erroneous rewards for residential consumers. This is due to larger uncertainty associated with residential customers and the inability of a Deterministic Approach to capturing such uncertainty. Different than the Deterministic Approach, we propose to conduct probabilistic baseline estimation and pay a customer over a period of time when the customer’s predicted error decreases due to reward aggregation. To achieve this goal, we analyze 12,000 residential customers’ data from PG&E and propose a Gaussian Process-based rewarding mechanism. Real data from PG&E and OhmConnect are used in validating the algorithm and showing fairer payment to residential customers. Finally, we provide a theoretical foundation that the proposed method is always better than the currently used industrial Approaches.
-
graphical model for state estimation in electric power systems
International Conference on Smart Grid Communications, 2013Co-Authors: Yang Weng, Rohit Negi, Marija IlicAbstract:This paper is motivated by major needs for fast and accurate on-line state estimation (SE) in the emerging electric energy systems, due to recent penetration of distributed green energy, distributed intelligence, and plug-in electric vehicles. Different from the traditional Deterministic Approach, this paper uses a probabilistic graphical model to account for these new uncertainties by efficient distributed state estimation. The proposed graphical model is able to discover and analyze unstructured information and it has been successfully deployed in statistical physics, computer vision, error control coding, and artificial intelligence. Specifically, this paper shows how to model the traditional power system state estimation problem in a probabilistic manner. Mature graphical model inference tools, such as belief propagation and variational belief propagation, are subsequently applied. Simulation results demonstrate better performance of SE over the traditional Deterministic Approach in terms of accuracy and computational time. Notably, the near-linear computational time of the proposed Approach enables the scalability of state estimation which is crucial in the operation of future large-scale smart grid.
Gang Lei - One of the best experts on this subject based on the ideXlab platform.
-
system level design optimization method for electrical drive systems robust Approach
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Gang Lei, Youguang Guo, Tianshi Wang, Jianguo Zhu, Shuhong WangAbstract:A system-level design optimization method under the framework of a Deterministic Approach was presented for electrical drive systems in our previous work, in which not only motors but also the integrated control schemes were designed and optimized to achieve good steady and dynamic performances. However, there are many unavoidable uncertainties (noise factors) in the industrial manufacturing process, such as material characteristics and manufacturing precision. These will result in big fluctuations for the product's reliability and quality in mass production, which are not investigated in the Deterministic Approach. Therefore, a robust Approach based on the technique of design for six sigma is presented for the system-level design optimization of drive systems to improve the reliability and quality of products in batch production in this work. Meanwhile, two system-level optimization frameworks are presented for the proposed method, namely, single-level (only at the system level) and multilevel frameworks. Finally, a drive system is investigated as an example, and detailed results are presented and discussed. It can be found that the reliability and quality levels of the investigated drive system have been greatly increased by using the proposed robust Approach.
