Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

Steady State Load Shedding to Prevent Blackout in the Power System using Artificial Bee Colony Algorithm

2015 ◽  
Vol 74 (1) ◽  
Author(s):  
R. Mageshvaran ◽  
T. Jayabarathi
Keyword(s):  
Steady State ◽  
Power System ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Optimal Solution ◽  
Load Shedding ◽  
Solution Quality ◽  
Bee Colony ◽  
The Difference

Real and reactive power deficiencies due to generation and overload contingencies in a power system may decline the system frequency and the system voltage. During these contingencies cascaded failures may occur which will lead to complete blackout of certain parts of the power system. Under such situations load shedding is considered as an emergency control action that is necessary to prevent a blackout in the power system by relieving overload in some parts of the system. The aim of this paper is to minimize the amount of load shed during generation and overload contingencies using a new meta-heuristic optimization algorithm known as artificial bee colony algorithm (ABC). The optimal solution for the problem of steady state load shedding is done by taking squares of the difference between the connected and supplied real and reactive power. The supplied active and reactive powers are treated as dependent variables modeled as functions of bus voltages only. The proposed algorithm is tested on IEEE 14, 30, 57, and 118 bus test systems. The applicability of the proposed method is demonstrated by comparison with the other conventional methods reported earlier in terms of solution quality and convergence properties. The comparison shows that the proposed algorithm gives better solutions and can be recommended as one of the optimization algorithms that can be used for optimal load shedding.

scholarly journals Improved Artificial Bee Colony Algorithm Applied to Neural Network Photovoltaic Power Generation Prediction Method

2020 ◽  
Vol 185 ◽  
pp. 01012
Author(s):  
Jiaxiong Zhu ◽  
Jiang Qiang ◽  
Chang Feng ◽  
Cao Jing
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Prediction Method ◽  
Weather Conditions ◽  
Network Parameter ◽  
Bee Colony ◽  
Photovoltaic Power

With the increase in the use of renewable energy, especially the development and utilization of solar energy resources, accurate photovoltaic power generation prediction technology will help the promotion of photovoltaic power generation. The amount of photovoltaic power generation depends on weather conditions, and it is easy to produce large fluctuations under different weather conditions. Its power generation has the characteristics of randomness, fluctuation and intermittency. In view of the shortcomings of the traditional BP neural network prediction method, this paper proposes an improved artificial bee colony algorithm. The improved artificial bee colony algorithm is used to optimize the network parameter weights in the traditional BP algorithm, and the two algorithms are merged in global iteration. Based on the characteristics of training light intensity, weather, temperature and historical power value of photovoltaic output power,a photovoltaic power generation prediction model is established. The simulation results show that the improved artificial bee colony algorithm in the neural network’s photovoltaic power generation forecast improves the accuracy and convergence speed of the traditional BP neural network convergence solution, and can provide more comprehensive information for grid power dispatch and control.

scholarly journals Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guiting Ren
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Bp Network ◽  
English Curriculum ◽  
Bee Colony ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Solution Accuracy

The traditional BP neural network has the disadvantages of easy falling into local minimum and slow convergence speed. Aiming at the shortcomings of BP neural network (BP neural network), an artificial bee colony algorithm (ABC) is proposed to cross-optimize the weight and threshold of BP network parameters. This study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. It includes the application of BP neural network algorithm in English course recommendation technology, English course teaching design mode, the application of BP neural network algorithm in English course, and the optimal combination of bee colony algorithm and BP neural network. After 4690 iterations, the neural network reaches the target accuracy, and the training is completed. At the same time, the prediction error of the model is less than 10%, which further shows that the performance of the prediction model is good. Therefore, the combination model is recommended in this paper. The results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network.

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