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Xin Wu
School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China

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Research article
Published: 24 June 2021 in International Journal of Remote Sensing
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In the process of using ground penetrating radar (GPR) to locate concealed power facilities such as underground cables, it is difficult to search the hyperbola generated by concealed facilities. To solve this problem, this paper proposes a two-step detection method to detect and locate the concealed facilities. First, the original echo image is pre-processed to enhance the weak signal and provide high-quality image for hyperbolic detection. And then, the first step of the two-step detection method is performed to extract the possible hyperbolic region. In this part, threshold processing method is used to reduce the clutter interference in the image, and the possible hyperbolic regions are detected and extracted based on the hyperbolic south open feature. In the second step, the trained faster Region-based Convolutional Neural Networks (faster-RCNN) is used to identify the extracted region, so as to obtain the position information of all hyperbolas in the echo image. By combining the hyperbolic detection results with the design drawings, the location of underground concealed facilities can be obtained. The proposed method is tested on the measured data, the experimental results show that the proposed method can accurately locate hyperbola from GPR data.

ACS Style

Xin Wu; Yuchen Gao; Lan You. Non-destructive location technology of concealed power facilities based on a two-step detection method. International Journal of Remote Sensing 2021, 42, 6607 -6627.

AMA Style

Xin Wu, Yuchen Gao, Lan You. Non-destructive location technology of concealed power facilities based on a two-step detection method. International Journal of Remote Sensing. 2021; 42 (17):6607-6627.

Chicago/Turabian Style

Xin Wu; Yuchen Gao; Lan You. 2021. "Non-destructive location technology of concealed power facilities based on a two-step detection method." International Journal of Remote Sensing 42, no. 17: 6607-6627.

Journal article
Published: 18 June 2020 in Processes
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Non-intrusive load monitoring (NILM) is an effective way to achieve demand-side measurement and energy efficiency optimization. This paper studies a method of non-intrusive on-line load monitoring under a high-frequency mode of electric data acquisition, which enables the NILM to be automated and in real-time, including the short-term construction of a dynamic signature library and continuous on-line load identification. Firstly, in the short initial operation phase, load separation and category determination are carried out to construct the load waveform library of the monitoring user. Then, the continuous load monitoring phase begins. Based on the data of each user’s signature library, the decomposition waveforms are classified by convolutional neural network models that are constructed to be suitable for each signature library in order to realize load identification. The real-time power consumption status of the load can be obtained continuously. In this paper, the electricity data of actual users are collected and used to perform the experiments, which show that the proposed method can construct the load signature library adaptively for different users. Meanwhile, the classification of the convolutional neural network model based on a library constructed in actual operation ensures the real-time and accuracy of load monitoring.

ACS Style

Xin Wu; Dian Jiao; Yu Du. Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network. Processes 2020, 8, 704 .

AMA Style

Xin Wu, Dian Jiao, Yu Du. Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network. Processes. 2020; 8 (6):704.

Chicago/Turabian Style

Xin Wu; Dian Jiao; Yu Du. 2020. "Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network." Processes 8, no. 6: 704.

Journal article
Published: 27 February 2020 in International Journal of Electrical Power & Energy Systems
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Nonintrusive load monitoring (NILM) is an effective way to measure the demand side of electricity consumption. An on-site NILM method in high-frequency acquisition mode was explored, which makes the entire process, including the construction of load-waveform decomposition and a short-term dynamic signature database, as well as continuous on-site load identification, automatic and executable in real time. First, a load decomposition model was established based on the additivity principle of load current to obtain the independent load waveform. According to the operational signatures of the load, there was no need to obtain prior data by conducting a preliminary experiment. The load type was judged by Bayesian classification model, and a dynamic load signature database was adaptively built for independent users. Based on the dynamic signature database, load identification was realized by optimization model, obtaining the electricity consumption of load in real time. The effectiveness of the method was verified by measuring electricity-consumption data. According to the experiment, the method can be automatically executed on-site to adapt to different users, and the dynamic signature database established improves the weak universality caused by establishing the database in advance. The fast optimization based on the signature database ensures the identification is efficient and accurate.

ACS Style

Xin Wu; Dian Jiao; Lan You. Nonintrusive on-site load-monitoring method with self-adaption. International Journal of Electrical Power & Energy Systems 2020, 119, 105934 .

AMA Style

Xin Wu, Dian Jiao, Lan You. Nonintrusive on-site load-monitoring method with self-adaption. International Journal of Electrical Power & Energy Systems. 2020; 119 ():105934.

Chicago/Turabian Style

Xin Wu; Dian Jiao; Lan You. 2020. "Nonintrusive on-site load-monitoring method with self-adaption." International Journal of Electrical Power & Energy Systems 119, no. : 105934.

Journal article
Published: 30 August 2019 in Applied Sciences
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Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

ACS Style

Jinying Yu; Yuchen Gao; Yuxin Wu; Dian Jiao; Chang Su; Xin Wu. Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration. Applied Sciences 2019, 9, 3558 .

AMA Style

Jinying Yu, Yuchen Gao, Yuxin Wu, Dian Jiao, Chang Su, Xin Wu. Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration. Applied Sciences. 2019; 9 (17):3558.

Chicago/Turabian Style

Jinying Yu; Yuchen Gao; Yuxin Wu; Dian Jiao; Chang Su; Xin Wu. 2019. "Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration." Applied Sciences 9, no. 17: 3558.

Journal article
Published: 19 July 2019 in Processes
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Air-conditionings have energy storage functions. Through reasonable aggregation control, the output tracking can be implemented for wind power with stronger fluctuation to enhance its utilization rate. Cloud technology and intelligent appliances enable the appliance vendor to implement information interaction with the air-conditioning through cloud platforms to realize flexible regulation. In this paper, a management and control method of air-conditioning based on cloud platform is established. Based on this structure, the air-conditionings are divided into several aggregation groups according to the similarity of parameters, and each group completes the consumption task collaboratively. The consumption evaluation model of the air-conditioning group is established. On this basis, the allocation problem on consumption task for the aggregated group is constructed to implement the optimal solution under the condition of guaranteeing the degree of completion and user comfort. Each group implements the control for air-conditioning inside the group through the sliding mode control model. The simulation experiment verifies that the algorithm can effectively follow the output of clean energy, while intervening less in the air-conditioning operation.

ACS Style

Kaixin Liang; Jinying Yu; Xin Wu; Yu; Wu. Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption. Processes 2019, 7, 467 .

AMA Style

Kaixin Liang, Jinying Yu, Xin Wu, Yu, Wu. Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption. Processes. 2019; 7 (7):467.

Chicago/Turabian Style

Kaixin Liang; Jinying Yu; Xin Wu; Yu; Wu. 2019. "Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption." Processes 7, no. 7: 467.

Journal article
Published: 03 June 2019 in Processes
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Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to disaggregate mixed signals, and then identify the independent load. This paper proposes a multi-label classification method using Random Forest (RF) as a learning algorithm for non-intrusive load identification. Multi-label classification can be used to determine which categories data belong to. This classification can help to identify the operation states of independent loads from mixed signals without disaggregation. The experiments are conducted in real environment and public data set respectively. Several basic electrical features are selected as the classification feature to build the classification model. These features are also compared to select the most suitable features for classification by feature importance parameters. The classification accuracy and F-score of the proposed method can reach 0.97 and 0.98, respectively.

ACS Style

Xin Wu; Yuchen Gao; Dian Jiao; Wu; Gao; Jiao. Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System. Processes 2019, 7, 337 .

AMA Style

Xin Wu, Yuchen Gao, Dian Jiao, Wu, Gao, Jiao. Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System. Processes. 2019; 7 (6):337.

Chicago/Turabian Style

Xin Wu; Yuchen Gao; Dian Jiao; Wu; Gao; Jiao. 2019. "Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System." Processes 7, no. 6: 337.

Letter
Published: 29 August 2018 in IEEJ Transactions on Electrical and Electronic Engineering
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With the development of new energy power generation, consumption of new energy has become a tricky problem. The aim of this letter is to develop an energy efficiency optimization method considering user comfort, specifically for household electric water heaters (HEWHs), which is effective to improve utilization associated with the new energy. The control cycle is calculated based on aggregate cycling load models, which is not affected by the comfortable conditions of using HEWHs. And, we develop a model to approach the target profile representing power needed to consume, then select the optimal combination of the model using the artificial immune algorithm, calculating the weight coefficient of components in combination using the least squares method. The validity of the method is verified using experiment. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

ACS Style

Xin Wu; Kaixin Liang. Energy efficiency optimization based on household electric water heater aggregation considering user comfort. IEEJ Transactions on Electrical and Electronic Engineering 2018, 13, 1677 -1678.

AMA Style

Xin Wu, Kaixin Liang. Energy efficiency optimization based on household electric water heater aggregation considering user comfort. IEEJ Transactions on Electrical and Electronic Engineering. 2018; 13 (11):1677-1678.

Chicago/Turabian Style

Xin Wu; Kaixin Liang. 2018. "Energy efficiency optimization based on household electric water heater aggregation considering user comfort." IEEJ Transactions on Electrical and Electronic Engineering 13, no. 11: 1677-1678.

Journal article
Published: 06 July 2018 in Applied Sciences
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With poor peak load regulating capacity, renewable energy generation is intermittent and fluctuating, which results in the insufficient acceptance capacity of the power grid. Based on the state-queuing model of aggregate air conditioning loads, this paper develops a control algorithm to achieve renewable energy consumption and output tracking. The load curves of the aggregate air conditioning loads can be controlled by changing the initial temperature distribution. Under different temperature distributions, the load curves represent a fixed fluctuation, which is the basis of output tracking. A virtual load curve set is established based on the state-queuing model. Regarding the load curves as basic signals, the expected renewable energy output can be tracked via an optimal combination of the basic load curves. The validity of the algorithm is testified by numerical emulation data.

ACS Style

Xin Wu; Kaixin Liang; Xiao Han. Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model. Applied Sciences 2018, 8, 1099 .

AMA Style

Xin Wu, Kaixin Liang, Xiao Han. Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model. Applied Sciences. 2018; 8 (7):1099.

Chicago/Turabian Style

Xin Wu; Kaixin Liang; Xiao Han. 2018. "Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model." Applied Sciences 8, no. 7: 1099.

Article
Published: 04 April 2018 in Applied Sciences
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Non-intrusive load monitoring (NILM) is a cost-effective technique for extracting device-level energy consumption information by monitoring the aggregated signal at the entrance of the electric power. With the large-scale deployment of smart metering, NILM should ideally be designed to operate purely on the low-rate data from smart meters. In this paper, an approach based on Graph Shift Quadratic Form constrained Active Power Disaggregation (GSQF-APD) is proposed, which is built upon matrix factorization and introduces graph shift quadratic form constraint according to piecewise smoothness of the power signal. In addition, a two-step iterative optimization method is designed to solve this problem. The first step minimizes the regularization term to find the signal with minimum variation, and then the second step uses the simulated annealing (SA) algorithm to iteratively minimize the objective function and constraint based on the total graph variation minimizer. Using one open-access dataset, the strength of GSQF-APD is demonstrated through three sets of experiments. The numerical results show the superior performance of GSQF-APD, with Graph Laplacian Quadratic Form constrained Active Power Disaggregation (GLQF-APD) and the state-of-the-art NILM methods as benchmarks.

ACS Style

Bing Qi; Liya Liu; Xin Wu. Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint. Applied Sciences 2018, 8, 554 .

AMA Style

Bing Qi, Liya Liu, Xin Wu. Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint. Applied Sciences. 2018; 8 (4):554.

Chicago/Turabian Style

Bing Qi; Liya Liu; Xin Wu. 2018. "Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint." Applied Sciences 8, no. 4: 554.

Article
Published: 08 December 2017 in IEEJ Transactions on Electrical and Electronic Engineering
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Nonintrusive load decomposition is the key technology to realize power load monitoring. The load decomposition is realized by the underdetermined solution of the current signal, and the complete current of each independent load is obtained. The model is optimized using the user's load operating habits and converted into a one-dimensional underdetermined problem to establish the solution model based on unit time interval to separate only two channels of signals from the acquisition signal. The optimal solution is obtained through the two-step iterative shrinkage threshold algorithm according to the frequency-domain sparsity of the current, so that each load is decomposed independently. The validity of the method is verified by the measured electrical data.

ACS Style

Xin Wu; Xiao Han. Nonintrusive load decomposition technology based on current sparsity underdetermined solution. IEEJ Transactions on Electrical and Electronic Engineering 2017, 12, S135 -S136.

AMA Style

Xin Wu, Xiao Han. Nonintrusive load decomposition technology based on current sparsity underdetermined solution. IEEJ Transactions on Electrical and Electronic Engineering. 2017; 12 ():S135-S136.

Chicago/Turabian Style

Xin Wu; Xiao Han. 2017. "Nonintrusive load decomposition technology based on current sparsity underdetermined solution." IEEJ Transactions on Electrical and Electronic Engineering 12, no. : S135-S136.

Article
Published: 05 June 2017 in IEEJ Transactions on Electrical and Electronic Engineering
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ACS Style

Xin Wu; Lu Han; Zhen Wang; Bing Qi. A nonintrusive fast residential load identification algorithm based on frequency-domain template filtering. IEEJ Transactions on Electrical and Electronic Engineering 2017, 12, S125 -S133.

AMA Style

Xin Wu, Lu Han, Zhen Wang, Bing Qi. A nonintrusive fast residential load identification algorithm based on frequency-domain template filtering. IEEJ Transactions on Electrical and Electronic Engineering. 2017; 12 ():S125-S133.

Chicago/Turabian Style

Xin Wu; Lu Han; Zhen Wang; Bing Qi. 2017. "A nonintrusive fast residential load identification algorithm based on frequency-domain template filtering." IEEJ Transactions on Electrical and Electronic Engineering 12, no. : S125-S133.

Journal article
Published: 01 January 2013 in Communications and Network
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As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.

ACS Style

Yi Sun; Lei Xu; Xin Wu; Minxuan Shen. A Chain Routing Algorithm Based on Traffic Prediction in Wireless Sensor Networks. Communications and Network 2013, 05, 504 -507.

AMA Style

Yi Sun, Lei Xu, Xin Wu, Minxuan Shen. A Chain Routing Algorithm Based on Traffic Prediction in Wireless Sensor Networks. Communications and Network. 2013; 05 (03):504-507.

Chicago/Turabian Style

Yi Sun; Lei Xu; Xin Wu; Minxuan Shen. 2013. "A Chain Routing Algorithm Based on Traffic Prediction in Wireless Sensor Networks." Communications and Network 05, no. 03: 504-507.