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
When using deep belief networks(DBN)to establish a fault diagnosis model,the objective function easily falls into a local optimum during the learning and training process due to random initialization of the DBN networ...When using deep belief networks(DBN)to establish a fault diagnosis model,the objective function easily falls into a local optimum during the learning and training process due to random initialization of the DBN network bias and weights,thereby affecting the computational efficiency.To address the problem,a fault diagnosis method based on a deep belief network optimized by genetic algorithm(GA-DBN)is proposed.The method uses the restricted Boltzmann machine reconstruction error to structure the fitness function,and uses the genetic algorithm to optimize the network bias and weight,thus improving the network accuracy and convergence speed.In the experiment,the performance of the model is analyzed from the aspects of reconstruction error,classification accuracy,and time-consuming size.The results are compared with those of back propagation optimized by the genetic algorithm,support vector machines,and DBN.It shows that the proposed method improves the generalization ability of traditional DBN,and has higher recognition accuracy of photovoltaic array faults.展开更多
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 the National Key Research and Development Program of China(2017YFB1201003-020)the Science and Technology Project of Gansu Province(18YF1FA058).
文摘When using deep belief networks(DBN)to establish a fault diagnosis model,the objective function easily falls into a local optimum during the learning and training process due to random initialization of the DBN network bias and weights,thereby affecting the computational efficiency.To address the problem,a fault diagnosis method based on a deep belief network optimized by genetic algorithm(GA-DBN)is proposed.The method uses the restricted Boltzmann machine reconstruction error to structure the fitness function,and uses the genetic algorithm to optimize the network bias and weight,thus improving the network accuracy and convergence speed.In the experiment,the performance of the model is analyzed from the aspects of reconstruction error,classification accuracy,and time-consuming size.The results are compared with those of back propagation optimized by the genetic algorithm,support vector machines,and DBN.It shows that the proposed method improves the generalization ability of traditional DBN,and has higher recognition accuracy of photovoltaic array faults.
