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Genetic algorithms approach to voltage optimization 遗传算法在电压优化中的应用
T. Haida, Y. Akimoto
The authors consider the use of genetic algorithms as a measure of voltage optimization of electric power system. Genetic algorithms are optimization and learning techniques based on natural selection and natural population genetics. A formation of a power system is encoded to a string of characters called an artificial chromosome the initial population of strings are generated at random, and then they are evolved by a genetic algorithm. The experiments with the prototype implementation are presented. These results verified the feasibility of genetic algorithms approach to power engineering.<>
作者考虑使用遗传算法作为电力系统电压优化的一种措施。遗传算法是基于自然选择和自然群体遗传学的优化和学习技术。电力系统的形成被编码为一串被称为人工染色体的字符,随机生成初始字符串,然后通过遗传算法进行进化。给出了样机实现的实验结果。这些结果验证了遗传算法方法在电力工程中的可行性。
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引用次数: 28
A neural networks approach to voltage security monitoring and control 基于神经网络的电压安全监测与控制
K. C. Hui, M. Short
Voltage collapse evaluation methods require elaborate computations to determine the existence of feasible load flow solutions in power systems. The time-consuming process of solving the stiff nonlinear system equations in these evaluation methods makes them inefficient for on-line monitoring of voltage collapse. The authors introduce an artificial neural network approach to voltage security monitoring and control. The neural network uses its association mechanism to approximate the complicated mathematical formulation of the voltage collapse phenomenon. The inherent parallel information processing nature of the neural network, which provides the capability of fast computation, enables the neural network approach to meet the rigorous demands of real-time monitoring and control. The IEEE 57 busbar system is used to demonstrate the applicability of the artificial neural network approach to the problem of voltage security monitoring and control in power systems.<>
电压崩溃评估方法需要详细的计算来确定电力系统中是否存在可行的潮流解。这些评估方法求解刚性非线性系统方程耗时长,不利于电压崩溃的在线监测。介绍了一种基于人工神经网络的电压安全监测与控制方法。神经网络利用其关联机制来逼近电压崩溃现象的复杂数学表达式。神经网络固有的并行信息处理特性,提供了快速的计算能力,使神经网络方法能够满足实时监测和控制的严格要求。以ieee57母线系统为例,验证了人工神经网络方法在电力系统电压安全监测与控制问题中的适用性。
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引用次数: 5
Unsupervised learning strategies for the detection and classification of transient phenomena on electric power distribution systems 配电系统暂态现象检测与分类的无监督学习策略
D.L. Lubkeman, C.D. Fallon, A. Girgis
A number of utilities are currently installing high-speed data acquisition equipment in their distribution substations. This equipment will make it possible to record the transient waveforms due to events such as low and high-impedance faults, capacitor switching, and load switching. The authors describe the potential of applying unsupervised learning strategies to the classification of the various events observed by a substation recorder. Several strategies are tested using simulation studies and the effectiveness of unsupervised learning is compared to current classification strategies as well as supervised learning.<>
一些公用事业公司目前正在其配电变电站安装高速数据采集设备。该设备将能够记录由于低阻抗和高阻抗故障、电容器开关和负载切换等事件而产生的瞬态波形。作者描述了应用无监督学习策略对变电站记录仪观察到的各种事件进行分类的潜力。使用模拟研究测试了几种策略,并将无监督学习的有效性与当前的分类策略以及监督学习进行了比较。
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引用次数: 11
Neural network based preventive control support system for power system stability enhancement 基于神经网络的电力系统稳定性增强预防控制支持系统
H. Saitoh, Y. Shimotori, J. Toyoda
The authors propose an application of a newly developed neural network to the preventive control of a power system. The purpose of the proposed control is to improve the damping effect of the system on electromechanical modes by reallocating load to generators. Since the neural network has flexible learning capability the authors apply it to identify the complex and nonlinear relation between the damping effect and the distribution of generating power. The trained neural network acts as the support system which aids an operator in performing the generating reallocation for enhancing the system stability. Furthermore, the authors develop a new type of neural network which can deal with the equal constraints about the output layer in the error-back-propagation type of neural network because it is important for the generating reallocation to satisfy the equal constraint about the energy balance between generation and load.<>
提出了一种新发展的神经网络在电力系统预防控制中的应用。所提出的控制的目的是通过将负载重新分配给发电机来改善系统对机电模式的阻尼效果。由于神经网络具有灵活的学习能力,作者将其应用于识别阻尼效应与发电功率分布之间的复杂非线性关系。训练后的神经网络作为支持系统,帮助操作员进行生成再分配,以提高系统的稳定性。此外,由于满足发电与负荷之间能量平衡的相等约束是发电再分配的重要条件,作者开发了一种新的神经网络,该神经网络可以处理误差反向传播型神经网络中关于输出层的相等约束。
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引用次数: 2
A hybrid neural network and expert system for monitoring fossil fuel power plants 化石燃料电厂监测的混合神经网络与专家系统
T. Kraft, K. Okagaki, R. Ishii, P. Surko, A. Brandon, A. DeWeese, S. Peterson, R. Bjordal
A fully recurrent neural network and a rule-based expert system are combined in a hybrid architecture to provide power plant operators with an intelligent on-line advisory system. Its purpose is to alert the operator to impending or occurring abnormal conditions related to the plant's boiler. The hybrid system is trained to provide a model of the boiler under normal operation, while the rules address a general set of diagnostic events. Deviation from normal conditions trigger rules to suggest corrective action. This system is intended to increase plant availability and efficiency by automatically deducing abnormal boiler conditions before they become critical.<>
将全递归神经网络和基于规则的专家系统结合在一个混合体系结构中,为电厂运营商提供智能在线咨询系统。其目的是提醒操作人员注意即将发生或正在发生的与电厂锅炉有关的异常情况。混合系统被训练为提供锅炉在正常运行下的模型,而规则则处理一组一般的诊断事件。偏离正常状态触发规则建议纠正措施。该系统旨在提高工厂的可用性和效率,通过自动扣除异常锅炉条件,在他们成为关键之前。
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引用次数: 6
Short term electric load forecasting using an adaptively trained layered perceptron 基于自适应训练分层感知器的短期电力负荷预测
M. El-Sharkawi, S. Oh, R. Marks, M. Damborg, C.M. Brace
The authors address electric load forecasting using artificial neural network (NN) technology. They summarize research for Puget Sound Power and Light Company. In this study, several structures for NNs are proposed and tested. Features extraction is implemented to capture strongly correlated variables to electric loads. The NN is compared to several forecasting models. Most of them are commercial codes. The NN performed as well as the best and most sophisticated commercial forecasting systems.<>
利用人工神经网络(NN)技术对电力负荷进行预测。他们总结了普吉特声光公司的研究。在本研究中,提出并测试了几种神经网络结构。实现特征提取以捕获与电力负荷强相关的变量。将神经网络与几种预测模型进行了比较。其中大部分是商业代码。神经网络的表现与最好和最复杂的商业预测系统一样好
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引用次数: 40
期刊
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems
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