Abductive reasoning network based diagnosis system for faultsection estimation in power systems

This paper presents an abductive reasoning network (ARN) for real-time fault section estimation in power systems. The proposed ARN handles complicated and knowledge-embedded relationships between the circuit breaker status (input) and the corresponding candidate fault section (output) using a hierarchical network with several layers of function nodes of simple low-order polynomials. The relay status is then further used to validate the final fault section. Test results confirm that the proposed diagnosis system can obtain rapid and accurate diagnosis results with flexibility and portability for diverse power system fault diagnosis. In addition, the proposed method performs better than the artificial neural networks (ANN) classification method both in developing the diagnosis system and in estimating the practical fault section. Moreover, this study demonstrates the feasibility of applying the proposed method to real power system fault diagnosis     

A differential relay for power transformers using intelligent tools

This paper presents alternative approaches using artificial neural networks (ANNs) for the protection of power transformers. A complete protection scheme was implemented. An ANN subroutine was used to discriminate internal faults from other situations, replacing the traditional Fourier method for harmonic restraint. In addition, a routine for reconstruction of saturated current signals based on recurrent ANNs is also proposed. The proposed methods were extensively tested and then compared to the traditional differential protection algorithm, showing promising results. The application of the ANN tools is a new and important stage in the differential relay analysis methodology for power transformer protection.     

基于循环神经网络的动态等值模型辨识

为了在系统稳定计算分析中简化和减小计算难度,用动态等值作为一种建模方法提出了一种基于循环神经网络的电力系统动态等值法并结合串并联辨识结构将其用于等值后模型的辨识。这种方法不需要预先建立确定的动态模型,仅靠边界节点的测量数据。循环神经网络的权值和结构决定了被等值外部模型的参数和结构。对IEEE的39节点进行的仿真结果表明了该方法能够捕捉原始系统的动态特性,有较高的精确性。     

基于人工神经网络的梯级水电厂日优化运行

提出了一种利用人工神经网络(ANN)进行梯级水电厂日优化运行研究的方法。既可用于制订 梯级日最优发电计划,又可用于梯级实时发电控制。为了加快神经网络的收敛速度,采用分 解网络技术,将一个复杂的网络分解为多个简单的网络。仿真结果表明,将神经网络应用于 这一领域,取得了较为满意的结果。     

Transmission loss allocation in a power market using artificial neural network

An artificial neural network (ANN)-based solution of the transmission loss allocation problem in a power market is suggested. The ANN proposed in this paper is a multilayer Perceptron network based on Levenberg–Marquardt algorithm and is capable of allocating losses to the agents identified as transactions in a power market. The network has a dynamic composition in the sense that it has to be trained afresh for determining the loss allocation of every transaction scenario instead of a network which is trained only once for all possible scenarios. The training dataset required is only a few in numbers and is filtered out from a large pool of data. The data pool includes the transactions values and their corresponding allocation of losses computed according to some established allocation method. Performance of the NN following game theoretic and proportional allocation of losses has been reported. Results are produced on standard test systems for bilateral and pool market operations.     

人工神经网络的混合电力补偿控制器设计

针对电力系统谐波补偿控制问题,基于人工神经网络技术,提出了电力补偿控制器设计方法,并给出了人工神经网络的结构、网络训练和测试的具体方法。实现了电力系统中谐波电流的自动检测,并根据谐波电流的具体情况实现补偿元件的自动切换以达到对谐波电流的补偿。仿真结果证明,利用神经网络技术实现对混合电力补偿器的控制是提高其动态性能的有效途径。     

Artificial neural networks in power system restoration

Power system restoration (PSR) has been a subject of study for many years. Many techniques were proposed to solve the limitations of the predetermined restoration guidelines and procedures used by a majority of system operators to restore a system following the occurrence of a wide area disturbance. This paper discusses limitations encountered in some currently used PSR techniques and a proposed improvement based on artificial neural networks (ANNs). The proposed scheme is tested on a 162-bus transmission system and compared with a breadth-search restoration scheme. The results indicate that the use of ANN in power system restoration is a feasible option that should be considered for real-time applications.     

Two approaches to transient stability-constrained optimal power flow

Formulation of transient stability-constrained optimal power flow (TSC-OPF) and finding a practical solution for the problem have gained much attention recently. In this paper, two approaches to include transient stability constraints in the OPF problem considering detailed dynamic models for generators and their controls are introduced. The first method is based on the maximum relative rotor angle deviation (MRRAD) of generators which suits systems that have specific requirements on MRRAD. The second method represents the transient stability margin of the system based on generators output power (GOP) and hence does not rely on MRRADs. The transient stability boundary can be represented by a nonlinear function of GOP. The Artificial Neural Network (ANN) curve-fitting tool is used to derive a mathematical formulation for the transient stability boundary (TSB). The closed form representation of the TSB is then inserted in the OPF problem as a new constraint. The proposed method is examined using the WSCC 9-bus, the New England 39-bus and the IEEE 300-bus test systems. The results indicate that the proposed methods lead to lower computational time in solving TSC-OPF which has been a serious challenge for this problem.     

A new power system transient stability assessment method based on Type-2 fuzzy neural network estimation

Transient stability assessment (TSA) of large power systems by the conventional method is a time consuming task. For each disturbance many nonlinear equations should be solved that makes the problem too complex and will lead to delayed decisions in providing the necessary control signals for controlling the system. Nowadays new methods which are devise artificial intelligence techniques are frequently used for TSA problem instead of traditional methods. Unfortunately these methods are suffering from uncertainty in input measurements. Therefore, there is a necessity to develop a reliable and fast online TSA to analyze the stability status of power systems when exposed to credible disturbances. We propose a direct method based on Type-2 fuzzy neural network for TSA problem. The Type-2 fuzzy logic can properly handle the uncertainty which is exist in the measurement of power system parameters. On the other hand a multilayer perceptron (MLP) neural network (NN) has expert knowledge and learning capability. The proposed hybrid method combines both of these capabilities to achieve an accurate estimation of critical clearing time (CCT). The CCT is an index of TSA in power systems. The Type-2 fuzzy NN is trained by fast resilient back-propagation algorithm. Also, in order to the proposed approach become scalable in a large power system, a NN based sensitivity analysis method is employed to select more effective input data. Moreover, In order to verify the performance of the proposed Type-2 fuzzy NN based method, it has been compared with a MLP NN method. Both of the methods are applied to the IEEE standard New England 10-machine 39-bus test system. The simulation results show the effectiveness of the proposed method in compare to the frequently used MLP NN based method in terms of accuracy and computational cost of CCT estimation for sample fault scenarios.     

基于模糊神经网络的电力系统预想事故排序方法

提出了一种基于模糊神经网络的电力系统预想事故排序新方法。该方法首先定义了反映预想事故严重程度的有功性能指标,同时构造3层人工神经网络(ANN)并采用误差反向传播(BP)算法加以训练;其次对ANN的输入用模糊神经网络进行特征选择,减少了输入层和中间隐含层的神经元个数及训练时间;最后通过IEEE 30节点系统验证了所提方法的有效性,仿真结果说明采用模糊神经网络进行输入量特征选择预处理可减少神经网络的训练时间。     

Artificial neural networks for load flow and external equivalents studies

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg–Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)–(d).     

On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks.     

Fault diagnosis system for tapped power transmission lines

This paper presents a design for a fault diagnosis system (FDS) for tapped HV/EHV power transmission lines. These lines have two different protection zones. The proposed approach reduces the cost and the complexity of the FDS for these types of lines. The FDS consists basically of fifteen artificial neural networks (ANNs). The FDS basic objectives are mainly: (1) the detection of the system fault; (2) the localization of the faulted zone; (3) the classification of the fault type; and finally (4) the identification of the faulted phase. This FDS is structured in a three hierarchical levels. In the first level, a preprocessing unit to the input data is performed. An ANN, in the second level, is designed in order to detect and zone localize the line faults. In the third level, two zone diagnosis systems (ZDS) are designed. Each ZDS is dedicated to one zone and consists of seven parallel-cascaded ANN's. Four-parallel ANN's are designed in order to achieve the fault type classification. While, the other three cascaded ANN's are designed mainly for the selection of the faulted phase. A smoothing unit is also configured to smooth out the output response of the proposed FDS.     

Linearization design method in class-F power amplifier using artificial neural network

This paper represents the design of a class-F power amplifier (PA), its artificial neural network (ANN) model and a PA linearization method. The designed PA operates at 1.8 GHz with gain of 12 dB and 1dB output compression point (P1dB) of 36 dBm. The proposed ANN model is used to predict the output power of designed class-F PA as a function of input and DC power. This model utilizes the designed class-F PA as a block, which could be used in a desired linearization circuit. In addition, the power added efficiency (PAE) and the other specifications of a PA, related to power can be predicted using the proposed model. A simple feedforward technique is used to improve the linearity of designed PA. For verification, this linearization method is compared with presented neural network model simulations. The results show the improvement of P1dB from 36 to 41 dBm, which is predicted using the proposed model. Also, the PAE of the final linearized circuit PA is predicted.     

人工神经网络和短时仿真结合的暂态安全评估事故筛选方法

结合人工神经网络（ＡＮＮ）和短时数字仿真提出一个用于在线暂态安全评估的事故筛选方法,将３层ＢＰ网络作为模式分类器,用来建立稳定评估结果和所选特征量之间的映射关系,在故障切除时刻终止的短时数字仿真被用来生成ＡＮＮ的输入量,每个ＡＮＮ处理一个特定的事故状态,使用一个半监督学习算法,ＡＮＮ可产生一个能够指示相对稳定度的连续分布的暂态稳定指标,基于这个连续分布的稳定指标,设置一个相对保守的分类门槛值,避免     

A method for determining pseudo‐measurement state values for topology observability of state estimation in power systems

This paper proposes a new technique for determining state values in power systems. Recently, it has been useful for carrying out state estimation with PMU (Phasor Measurement Unit) data. The authors have developed a method for determining state values with an artificial neural network (ANN) considering topology observability in power systems. The ANN has the advantage of approximating nonlinear functions with high precision. The method evaluates pseudo‐measurement state values of data which are lost in power systems. The method has been successfully applied to the IEEE 14‐bus system. © 2012 Wiley Periodicals, Inc. Electr Eng Jpn, 179(2): 27–34, 2012; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21235     

An Integrated Approach to Economic Emission Load Dispatching Using Neural Network and Goal-Attainment Methods

This paper presents an integrated approach comprising artificial neural network (ANN) and goal-attainment (GA) methods to economic emission load dispatching (EELD) in power system operation and scheduling phases. The GA method, which is one of the most powerful tools for multiobjective optimization problems, is quantitatively performed to grasp trade-off relations between the two conflicting objectives (economy and emission impact) in the training set creation phase. Finally, a radial basis function ANN is trained by the orthogonal least squares learning algorithm to reach the optimal generations. The ANN models so developed have been tested to solve EELD problem on 6-bus and 71-bus power systems. The test results demonstrate that the proposed approach is capable of obtaining well-coordinated optimal solutions suitable both in accuracy and speed while allowing flexibility in the operation of the generators.     

Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines

Feed-forward (FF) artificial neural networks (ANN) and radial basis function (RBF) ANN methods were addressed for evaluating the lightning performance of high voltage transmission lines. Several structures, learning algorithms and transfer functions were tested in order to produce a model with the best generalizing ability. Actual input and output data, collected from operating Hellenic high voltage transmission lines, as well as simulated output data were used in the training, validation and testing process. The aims of the paper are to describe in detail and compare the proposed FF and RBF ANN models, to state their advantages and disadvantages and to present results obtained by their application on operating Hellenic transmission lines of 150 kV and 400 kV. The ANN results are also compared with results obtained using conventional methods and real records of outage rate showing a quite satisfactory agreement. The proposed ANN methods can be used by electric power utilities as useful tools for the design of electric power systems, alternative to the conventional analytical methods.     

基于最优光伏阵列重构的电网调频策略研究

为实现光伏电站功率输出最大化以及功率输出与调频(FR)信号之间的功率偏差最小化,提出了一种新型光伏电站最优阵列重构(OAR)模型.为快速获取最优Pareto前沿,采用了一种寻优性能高效的多目标黑猩猩优化器(MOBO).采用了一种多准则妥协解排序法(VIKOR)的决策方法,从所获取的Pareto前沿中确定最佳折衷解.为验...     

An intelligent power factor corrector for power system using artificial neural networks

An intelligent power factor correction approach based on artificial neural networks (ANN) is introduced. Four learning algorithms, backpropagation (BP), delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. The best test results obtained from the ANN compensators trained with the four learning algorithms were first achieved. The parameters belonging to each neural compensator obtained from an off-line training were then inserted into a microcontroller for on-line usage. The results have shown that the selected intelligent compensators developed in this work might overcome the problems occurred in the literature providing accurate, simple and low-cost solution for compensation.