In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is...In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.展开更多
This study proposes a novel approach for estimating automobile insurance loss reserves utilizing Artificial Neural Network (ANN) techniques integrated with actuarial data intelligence. The model aims to address the ch...This study proposes a novel approach for estimating automobile insurance loss reserves utilizing Artificial Neural Network (ANN) techniques integrated with actuarial data intelligence. The model aims to address the challenges of accurately predicting insurance claim frequencies, severities, and overall loss reserves while accounting for inflation adjustments. Through comprehensive data analysis and model development, this research explores the effectiveness of ANN methodologies in capturing complex nonlinear relationships within insurance data. The study leverages a data set comprising automobile insurance policyholder information, claim history, and economic indicators to train and validate the ANN-based reserving model. Key aspects of the methodology include data preprocessing techniques such as one-hot encoding and scaling, followed by the construction of frequency, severity, and overall loss reserving models using ANN architectures. Moreover, the model incorporates inflation adjustment factors to ensure the accurate estimation of future loss reserves in real terms. Results from the study demonstrate the superior predictive performance of the ANN-based reserving model compared to traditional actuarial methods, with substantial improvements in accuracy and robustness. Furthermore, the model’s ability to adapt to changing market conditions and regulatory requirements, such as IFRS17, highlights its practical relevance in the insurance industry. The findings of this research contribute to the advancement of actuarial science and provide valuable insights for insurance companies seeking more accurate and efficient loss reserving techniques. The proposed ANN-based approach offers a promising avenue for enhancing risk management practices and optimizing financial decision-making processes in the automobile insurance sector.展开更多
The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such a...The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.展开更多
In real complex systems, the limited storage capacity of physical devices often results in the loss of data. We study the effect of buffer size on packet loss threshold in scale-free networks. A new order parameter is...In real complex systems, the limited storage capacity of physical devices often results in the loss of data. We study the effect of buffer size on packet loss threshold in scale-free networks. A new order parameter is proposed to characterize the packet loss threshold. Our results show that the packet loss threshold can be optimized with a relative small buffer size. Meanwhile, a large buffer size will increase the travel time. Furthermore, we propose a Buffered-Shortest-Path-First(BSPF) queuing strategy. Compared to the traditional First-In-First-Out(FIFO) strategy, BSPF can not only increase the packet loss threshold but can also significantly decrease the travel length and travel time in both identical and heterogeneous node capacity cases. Our study will help to improve the traffic performance in finite buffer networks.展开更多
Water supply network losses are an international problem especially in countries suffering from water scarcity like Jordan. Jordan is one of the poorest countries in its water resources and it is estimated to be below...Water supply network losses are an international problem especially in countries suffering from water scarcity like Jordan. Jordan is one of the poorest countries in its water resources and it is estimated to be below the water poverty line. Jordan is located in the Middle East and has a surface area of approximately 90,000 km2. Its population is around 6.3 million and it is estimated that the population will be 7.8 million in 2022. The gap between water supply and demand is widening due to development and a relatively high population growth rate. In addition, global climate change is expected to intensify the water shortage problem in Jordan. Thirteen years of complete records obtained from the Ministry of Water and Irrigation were analyzed. According to these records, water losses in Jordan reach about 50%. In view of the evaluation of the data and the case study conducted in this research, it is believed that Jordan can overcome the water shortage problem by adopting a water demand management strategy. In this context, efforts should be focused on reducing water losses. If this is achieved, it will save huge quantities of water and revenue.展开更多
Network losses allocation is one of the major problems in the market environment. The quadric function of the injected nodal power is used in this paper as a representation for network losses, which are allocated fair...Network losses allocation is one of the major problems in the market environment. The quadric function of the injected nodal power is used in this paper as a representation for network losses, which are allocated fairly using the called market equilibrium principle while the bidding curves are corrected. The power market equilibrium is simulated as three different models that can be solved simply by the optimal power flow algorithm combining the generation scheduling problem with network losses allocation. The case study is made at an IEEE-30 nodes system and a perfect result is proved in this paper.展开更多
With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization p...With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.展开更多
On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Mal...On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.展开更多
This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems,...This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.展开更多
In this paper,a fault tolerant control with the consideration of actuator fault for a networked control system （NCS） with packet loss is addressed.The NCS with data packet loss can be described as a switched system ...In this paper,a fault tolerant control with the consideration of actuator fault for a networked control system （NCS） with packet loss is addressed.The NCS with data packet loss can be described as a switched system model.Packet loss dependent Lyapunov function is used and a fault tolerant controller is proposed respectively for arbitrary packet loss process and Markovian packet loss process.Considering a controlled plant with external energy-bounded disturbance,a robust H ∞ fault tolerant controller is designed for the NCS.These results are also expanded to the NCS with packet loss and networked-induced delay.Numerical examples are given to illustrate the effectiveness of the proposed design method.展开更多
According to complexity and multiplicity of the post-earthquake fire, the loss forecasting model of earthquake fire is established by using radial basis function neural network with adaptability, self-learning and fau...According to complexity and multiplicity of the post-earthquake fire, the loss forecasting model of earthquake fire is established by using radial basis function neural network with adaptability, self-learning and fault-tolerant based on the historical information. The applicability and validity of the model is manifested through testing and discussion. A simple and available method is provided for the prediction of losses of other natural disaster.展开更多
A stability problem of the linear networked control systems(NCSs) with multisensor having differen data missing rates is investigated in this paper.Each sensor of the multiple sensor-controller communication channels ...A stability problem of the linear networked control systems(NCSs) with multisensor having differen data missing rates is investigated in this paper.Each sensor of the multiple sensor-controller communication channels is assumed to have an individual stochastic data missing rate.The stochastic data missing is described by a Bernoulli binary distribution.Sufficient conditions are given for the closed-loop linear NCS which is exponentially stable in the mean square sense as the existence of random multiple data missing.The stability problem could b disposed by the MATLAB linear matrix inequality(LMI) tool easily.A simulation case is provided to illustrat the validity of the presented LMI approach.展开更多
Providing reliable multicast service is very challenging in Ad Hoc networks. In this paper, we propose an efficient loss recovery scheme for reliable multicast (CoreRM). Our basic idea is to apply the notion of cooper...Providing reliable multicast service is very challenging in Ad Hoc networks. In this paper, we propose an efficient loss recovery scheme for reliable multicast (CoreRM). Our basic idea is to apply the notion of cooperative communications to support local loss recovery in multicast. A receiver node experiencing a packet loss tries to recover the lost packet through progressively cooperating with neighboring nodes, upstream nodes or even source node. In order to reduce recovery latency and retransmission overhead, CoreRM caches not only data packets but also the path which could be used for future possible use to expedite the loss recovery process. Both analytical and simulation results reveal that CoreRM significantly improves the reliable multicast performance in terms of delivery ratio, throughput and recovery latency compared with UDP and PGM.展开更多
Image processing technique was employed to analyze pitting corrosion morphologies of 304 stainless steel exposed to FeCl3 environments. BP neural network models were developed for the prediction of pitting corrosion m...Image processing technique was employed to analyze pitting corrosion morphologies of 304 stainless steel exposed to FeCl3 environments. BP neural network models were developed for the prediction of pitting corrosion mass loss using the obtained data of the total and the average pit areas which were extracted from pitting binary image. The results showed that the predicted results obtained by the 2-5-1 type BP neural network model are in good agreement with the experimental data of pitting corrosion mass loss. The maximum relative error of prediction is 6.78%.展开更多
基金supported by the National Natural Science Foundation of China(52177081).
文摘In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.
文摘This study proposes a novel approach for estimating automobile insurance loss reserves utilizing Artificial Neural Network (ANN) techniques integrated with actuarial data intelligence. The model aims to address the challenges of accurately predicting insurance claim frequencies, severities, and overall loss reserves while accounting for inflation adjustments. Through comprehensive data analysis and model development, this research explores the effectiveness of ANN methodologies in capturing complex nonlinear relationships within insurance data. The study leverages a data set comprising automobile insurance policyholder information, claim history, and economic indicators to train and validate the ANN-based reserving model. Key aspects of the methodology include data preprocessing techniques such as one-hot encoding and scaling, followed by the construction of frequency, severity, and overall loss reserving models using ANN architectures. Moreover, the model incorporates inflation adjustment factors to ensure the accurate estimation of future loss reserves in real terms. Results from the study demonstrate the superior predictive performance of the ANN-based reserving model compared to traditional actuarial methods, with substantial improvements in accuracy and robustness. Furthermore, the model’s ability to adapt to changing market conditions and regulatory requirements, such as IFRS17, highlights its practical relevance in the insurance industry. The findings of this research contribute to the advancement of actuarial science and provide valuable insights for insurance companies seeking more accurate and efficient loss reserving techniques. The proposed ANN-based approach offers a promising avenue for enhancing risk management practices and optimizing financial decision-making processes in the automobile insurance sector.
文摘The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.
基金Project supported by the National Key Research and Development Program of China(Grant No.2016YFC0802508)the National Natural Science Foundation of China(Grant Nos.11672289 and 61503355)the support from the Chinese Scholarship Council
文摘In real complex systems, the limited storage capacity of physical devices often results in the loss of data. We study the effect of buffer size on packet loss threshold in scale-free networks. A new order parameter is proposed to characterize the packet loss threshold. Our results show that the packet loss threshold can be optimized with a relative small buffer size. Meanwhile, a large buffer size will increase the travel time. Furthermore, we propose a Buffered-Shortest-Path-First(BSPF) queuing strategy. Compared to the traditional First-In-First-Out(FIFO) strategy, BSPF can not only increase the packet loss threshold but can also significantly decrease the travel length and travel time in both identical and heterogeneous node capacity cases. Our study will help to improve the traffic performance in finite buffer networks.
文摘Water supply network losses are an international problem especially in countries suffering from water scarcity like Jordan. Jordan is one of the poorest countries in its water resources and it is estimated to be below the water poverty line. Jordan is located in the Middle East and has a surface area of approximately 90,000 km2. Its population is around 6.3 million and it is estimated that the population will be 7.8 million in 2022. The gap between water supply and demand is widening due to development and a relatively high population growth rate. In addition, global climate change is expected to intensify the water shortage problem in Jordan. Thirteen years of complete records obtained from the Ministry of Water and Irrigation were analyzed. According to these records, water losses in Jordan reach about 50%. In view of the evaluation of the data and the case study conducted in this research, it is believed that Jordan can overcome the water shortage problem by adopting a water demand management strategy. In this context, efforts should be focused on reducing water losses. If this is achieved, it will save huge quantities of water and revenue.
文摘Network losses allocation is one of the major problems in the market environment. The quadric function of the injected nodal power is used in this paper as a representation for network losses, which are allocated fairly using the called market equilibrium principle while the bidding curves are corrected. The power market equilibrium is simulated as three different models that can be solved simply by the optimal power flow algorithm combining the generation scheduling problem with network losses allocation. The case study is made at an IEEE-30 nodes system and a perfect result is proved in this paper.
基金This research is supported by the Science and Technology Program of Gansu Province(No.23JRRA880).
文摘With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.
基金Supported by Brilliant Youth Fund in Hebei Province
文摘On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.
基金Project supported by the Key Program for the National Natural Science Foundation of China(Grant No.61333003)the General Program for the National Natural Science Foundation of China(Grant No.61273104)
文摘This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.
基金supported by National Natural Science Foundation of China (No. 60874052)
文摘In this paper,a fault tolerant control with the consideration of actuator fault for a networked control system （NCS） with packet loss is addressed.The NCS with data packet loss can be described as a switched system model.Packet loss dependent Lyapunov function is used and a fault tolerant controller is proposed respectively for arbitrary packet loss process and Markovian packet loss process.Considering a controlled plant with external energy-bounded disturbance,a robust H ∞ fault tolerant controller is designed for the NCS.These results are also expanded to the NCS with packet loss and networked-induced delay.Numerical examples are given to illustrate the effectiveness of the proposed design method.
文摘According to complexity and multiplicity of the post-earthquake fire, the loss forecasting model of earthquake fire is established by using radial basis function neural network with adaptability, self-learning and fault-tolerant based on the historical information. The applicability and validity of the model is manifested through testing and discussion. A simple and available method is provided for the prediction of losses of other natural disaster.
基金the National Natural Science Foundation of China(No.U1204515)the Foundation of Young Teachers in Colleges and Universities of Shanghai(No.ZZSDJ12002)the Shanghai Municipal Natural Science Foundation(No.14ZR1417200)
文摘A stability problem of the linear networked control systems(NCSs) with multisensor having differen data missing rates is investigated in this paper.Each sensor of the multiple sensor-controller communication channels is assumed to have an individual stochastic data missing rate.The stochastic data missing is described by a Bernoulli binary distribution.Sufficient conditions are given for the closed-loop linear NCS which is exponentially stable in the mean square sense as the existence of random multiple data missing.The stability problem could b disposed by the MATLAB linear matrix inequality(LMI) tool easily.A simulation case is provided to illustrat the validity of the presented LMI approach.
文摘Providing reliable multicast service is very challenging in Ad Hoc networks. In this paper, we propose an efficient loss recovery scheme for reliable multicast (CoreRM). Our basic idea is to apply the notion of cooperative communications to support local loss recovery in multicast. A receiver node experiencing a packet loss tries to recover the lost packet through progressively cooperating with neighboring nodes, upstream nodes or even source node. In order to reduce recovery latency and retransmission overhead, CoreRM caches not only data packets but also the path which could be used for future possible use to expedite the loss recovery process. Both analytical and simulation results reveal that CoreRM significantly improves the reliable multicast performance in terms of delivery ratio, throughput and recovery latency compared with UDP and PGM.
文摘Image processing technique was employed to analyze pitting corrosion morphologies of 304 stainless steel exposed to FeCl3 environments. BP neural network models were developed for the prediction of pitting corrosion mass loss using the obtained data of the total and the average pit areas which were extracted from pitting binary image. The results showed that the predicted results obtained by the 2-5-1 type BP neural network model are in good agreement with the experimental data of pitting corrosion mass loss. The maximum relative error of prediction is 6.78%.
