Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization r...Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.展开更多
In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this stud...In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.展开更多
Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network. Because the character of wireless se...Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network. Because the character of wireless sensor networks is restrictive energy, this paper proposes a distributed power control algorithm based on game theory for wireless sensor networks which objects of which are reducing power consumption and decreasing overhead and increasing network lifetime. The game theory and OPNET simulation shows that the power control algorithm converges to a Nash Equilibrium when decisions are updated according to a better response dynamic.展开更多
Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune...Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.展开更多
Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink...Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs＇ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.展开更多
Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam construction.In order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered ...Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam construction.In order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered for construction of a power plant site in Iran.First we gain the residual anomalies through bouger anomalies and then we design an Artificial Neural Network(ANN)which is trained by a set of training data.The ANN was tested for both synthetic and real data.For real data some suitable features are derivate from residual anomalies and applied to展开更多
The data gathering manner of wireless sensor networks, in which data is forwarded towards the sink node, would cause the nodes near the sink node to transmit more data than those far from it. Most data gathering mecha...The data gathering manner of wireless sensor networks, in which data is forwarded towards the sink node, would cause the nodes near the sink node to transmit more data than those far from it. Most data gathering mechanisms nowdo not do well in balancing the energy consumption among nodes with different distances to the sink, thus they can hardly avoid the problem that nodes near the sink consume energy more quickly, which may cause the network rupture from the sink node. This paper presents a data gathering mechanism called PODA, which grades the output power of nodes according to their distances from the sink node. PODA balances energy consumption by setting the nodes near the sink with lower output power and the nodes far from the sink with higher output power. Simulation results show that the PODA mechanism can achieve even energy consumption in the entire network, improve energy efficiency and prolong the network lifetime.展开更多
文摘Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.
基金Supported by China Postdoctoral Science Foundation(20090460873)
文摘In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.
文摘Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network. Because the character of wireless sensor networks is restrictive energy, this paper proposes a distributed power control algorithm based on game theory for wireless sensor networks which objects of which are reducing power consumption and decreasing overhead and increasing network lifetime. The game theory and OPNET simulation shows that the power control algorithm converges to a Nash Equilibrium when decisions are updated according to a better response dynamic.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.
基金supported by the National Natural Science Foundation of China (7070102571071105)+2 种基金the Program for New Century Excellent Talents in Universities of China (NCET-08-0396)the National Science Fund for Distinguished Young Scholars of China (70925005)the Program for Changjiang Scholars and Innovative Research Team in University (IRT/028)
文摘Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs＇ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.
文摘Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam construction.In order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered for construction of a power plant site in Iran.First we gain the residual anomalies through bouger anomalies and then we design an Artificial Neural Network(ANN)which is trained by a set of training data.The ANN was tested for both synthetic and real data.For real data some suitable features are derivate from residual anomalies and applied to
基金Supported by National Natural Science Foundation of P. R. China (60434030, 60673178)
文摘The data gathering manner of wireless sensor networks, in which data is forwarded towards the sink node, would cause the nodes near the sink node to transmit more data than those far from it. Most data gathering mechanisms nowdo not do well in balancing the energy consumption among nodes with different distances to the sink, thus they can hardly avoid the problem that nodes near the sink consume energy more quickly, which may cause the network rupture from the sink node. This paper presents a data gathering mechanism called PODA, which grades the output power of nodes according to their distances from the sink node. PODA balances energy consumption by setting the nodes near the sink with lower output power and the nodes far from the sink with higher output power. Simulation results show that the PODA mechanism can achieve even energy consumption in the entire network, improve energy efficiency and prolong the network lifetime.
