基于连续潮流的输电陬可用输电能力计算

提出一种基于连续潮流的线性迭代法计算输电网可用输电能力ATC(Avajlable Transfer Capability)，详细推导了该算法的数学模型，并给出计算方法及流程，将算法应用到IEEE—30节点系统，结果表明该方法具有一定的实用性、可靠性和有效性。同时，将基于连续潮流法与直流灵敏度法的计算精度进行比较，结果表明该方法精度高；此外讨论了负荷参数λ取固定增长步长与变增长步长两种情况下的计算精度与计算速度，结果表明两者计算精度差不多，但后者计算速度快一些。     

计及FACTS装置的可用输电能力计算

利用功率注入法,建立广义统一潮流控制器(generalized unified power flow controller,GUPFC)和线间潮流控制器(interline power flow controller,IPFC)的数学模型。将GUPFC和IPFC的目标控制约束及运行约束即内部功率平衡约束和考虑等效功率注入模型的潮流约束嵌入到最优潮流计算模型中,得到计及GUPFC和IPFC的可用输电能力(available transfer capability,ATC)计算模型,并利用跟踪中心轨迹内点法对模型进行求解。IEEE-30节点系统的仿真计算显示GUPFC对节点电压和多条线路甚至某一子网络潮流的灵活控制能力及IPFC对线间潮流的合理分配能力;同时验证模型和算法的有效性和可行性。     

基于线性化最优潮流的电网可用输电能力计算

可用输电能力(ATC)是评价互联电网运行安全稳定裕度的重要测度指标。针对交流最优潮流(OPF)模型计算ATC时在收敛性方面的不足,提出一种计及电压和网损的线性潮流方程,在此基础上构造用于ATC计算的OPF计算模型,并通过先估计网损、再求解优化问题的2步求解策略得到ATC的计算结果。IEEE 39节点系统的算例分析验证了所提方法的正确性和有效性。     

应用粒子群优化算法求解可用输电能力

在电力市场环境下,诸多问题(例如实时电价、网络阻塞等)都需要最优潮流作为理想的工具.本文以最优潮流为基础,应用一种简单有效、且收敛性很好的演化计算算法--粒子群优化算法(PSO)进行可用输电能力(ATC)问题的求解.根据约束条件的越限量大小,动态地调整罚函数,在保证全局搜索能力的基础上改进了收敛速度.应用此算法对IEEE-30节点系统进行了可用输电能力计算,并与传统的最优潮流算法进行了比较,结果表明该算法的有效性,具有实用意义.     

基于连续潮流的输电网可用输电能力计算

提出一种基于连续潮流的线性迭代法计算输电网可用输电能力ATC(AvailableTransferCa鄄pability),详细推导了该算法的数学模型,并给出计算方法及流程,将算法应用到IEEE-30节点系统,结果表明该方法具有一定的实用性、可靠性和有效性。同时,将基于连续潮流法与直流灵敏度法的计算精度进行比较,结果表明该方法精度高;此外讨论了负荷参数λ取固定增长步长与变增长步长两种情况下的计算精度与计算速度,结果表明两者计算精度差不多,但后者计算速度快一些。     

计及发电机报价和负荷消费意愿的可用输电能力计算

提出一种在电力市场环境下考虑经济性约束的区域间可用输电能力的计算方法,建立了在系统每一运行点都能根据发电机组报价经济地分配其有功出力并限制各节点电价在某一可行范围内的数学模型,其目标函数是购电区域内负荷的增长量最大,约束条件为系统的安全性约束和经济性约束,更符合电力市场的实际情况.采用主从递阶决策求解数学模型,并利用非线性互补问题函数的近似半光滑牛顿算法处理底层优化问题所带来的不等式约束.以IEEE 30节点系统为例进行仿真并对计算结果予以分析,验证了所提出的方法的合理性.     

计及静态电压稳定约束的交直流系统可用输电能力

为考虑不同控制方式下直流系统对交流潮流的影响,采用顺序求解法计算交直流系统潮流时,对交流系统雅可比矩阵的特殊节点元素进行了有效修正。将电压稳定局部指标L进行合理简化,简化后的L指标能够避免电压相量复数运算,有效提高了计算速度。在简化的L指标基础上,提出了计及电压稳定性约束的交直流系统可用输电能力优化模型。采用非线性原–对偶于内点法对优化模型进行求解,通过计算L指标研究不同的电压稳定裕度对系统可用输电能力和电压稳定性的影响。算例结果验证了简化L指标的合理性和优化模型的有效性。     

改进粒子群优化算法的概率可用输电能力研究

研究了基于改进粒子群优化算法的概率可用输电能力的计算问题。建立了基于最优潮流的区域间可用输电能力模型；构造了适合可用输电能力研究的改进粒子群算法，并提出了自适应调整惯性权重的策略，该策略有效改善了算法的适应性和收敛速度；针对改进粒子群算法的搜索特点，动态调节罚函数。在此基础上对经模态分析方法选出的系统严重故障进行可用输电能力计算，并对各种故障出现概率和相应的可用输电能力进行概率统计分析，得到具有概率性质的可用输电能力。对IEEE-30节点系统的仿真计算验证了文中所提方法的合理性和有效性。     

一种考虑暂态稳定约束的可用输电能力计算的新方法

提出了一种考虑暂态稳定约束的可用输电能力计算的新方法。采用约束转换技术处理系统动态方程中的微分方程，将函数空间的优化问题转换为Euclidean空间的优化问题，并利用非线性互补方法求解。该方法以原一对偶内点法为基础，通过引入一个特性函数来处理互补性条件，克服了原对偶内点法在每次迭代中都必须保持正方向的缺点，在效率上有很大的提高。该文以7节点系统和36节点系统的计算结果为例证实了该方法的有效性和合理性。     

基于径向基神经网络的输电线路动态容量在线预测

在线预测输电线路的动态热容量,合理安排负荷高峰时期运行方式和调度管理,对输电线路的安全和经济运行具有重要意义,同时也对确定风电等间歇式可再生能源的接入容量提供技术支持.为此,提出了利用径向基神经网络实现线路动态容量的在线预测方法.该方法首先利用径向基神经网络进行风速和日照辐射温度的在线学习和预测,基于IEEE 738标准进行输电线路动态容量的在线短期预测.利用典型的夏季和冬季实测数据进行动态容量预测后发现,预测未来1、2、4 h的动态容量的最大相对误差分别在10%、20%、40%以内.将短期的负荷预测与该方法结合起来,可为电力紧张地区和负荷高峰时期的智能调度提供决策支持.     

A radial basis function neural network controller for UPFC

This paper presents the design of radial basis function neural network controllers (RBFNN) for UPFC to improve the transient stability performance of a power system. The RBFNN uses either a single neuron or multi-neuron architecture and the parameters are dynamically adjusted using an error surface derived from active or reactive power/voltage deviations at the UPFC injection bus. The performance of the new single neuron controller is evaluated using both single-machine infinite-bus and three-machine power systems subjected to various transient disturbances. In the case of three-machine 8-bus power system, the performance of the single neuron RBF controller is compared with a BP (backpropagation) algorithm based multi-layered ANN controller. Further it is seen that by using a multi-input multi-neuron RBF controller, instead of a single neuron one, the critical clearing time and damping performance are improved. The new RBFNN controller for UPFC exhibits a superior damping performance in comparison to the existing PI controllers. Its simple architecture reduces the computational burden thereby making it attractive for real-time implementation     

Probabilistic approach to available transfer capability calculation

This paper suggests a probabilistic approach to calculate available transfer capability in the interconnected network. Calculation of available transfer capability is complicated because it involves determination of total transfer capability, transmission reliability margin and capacity benefit margin.In the suggested available transfer capability quantification method, total transfer capability is determined by the continuation power flow process. Transmission reliability margin and capacity benefit margin are evaluated by probabilistic load flow and Monte Carlo simulation, respectively.Proposed method is applied to a modified IEEE 72-bus 3-area system to calculate available transfer capability on two different time spans. Results of the case study show that suggested probabilistic approach can offer operational flexibility for system operators to consider system and market uncertainties.     

基于多输出衰减径向基函数神经网络的电网故障诊断

为提高电网故障诊断神经网络模型的构建速度,提出了一种基于多输出衰减径向基函数（Multi-output Decay Radial Basis Function, MDRBF）神经网络的故障诊断方法。DRBF神经网络不需训练即能以任意精度一致逼近任意连续多变量函数。介绍了单输出DRBF（Single-output DRBF, SDRBF）神经网络,分析了其存在的不足,即只能处理单输出变量问题,不能直接应用于电网故障诊断。在此基础上,根据电网元件的故障特点,提出了将SDRBF神经网络演变为多输出DRBF（Mu     

Application of minimal radial basis function neural network todistance protection

The paper presents a new approach for the protection of power transmission lines using a minimal radial basis function neural network (MRBFNN). This type of RBF neural network uses a sequential learning procedure to determine the optimum number of neurons in the hidden layer without resorting to trial and error. The input data to this network comprises fundamental peak values of relaying point voltage and current signals, the zero-sequence component of current and system operating frequency. These input variables are obtained by a Kalman filtering approach. Further, the parameters of the network are adjusted using a variant of extended Kalman filter known as locally iterated Kalman filter to produce better accuracy in the output for harmonics, DC offset and noise in the input data. The number of training patterns and the training time are drastically reduced and significant accuracy is achieved in different types of fault classification and location in transmission lines using computer simulated tests     

Fast voltage contingency screening using radial basis function neural network

Power system security is one of the vital concerns in competitive electricity markets due to the delineation of the system controller and the generation owner. This paper presents an approach based on radial basis function neural network (RBFN) to rank the contingencies expected to cause steady state bus voltage violations. Euclidean distance-based clustering technique has been employed to select the number of hidden (RBF) units and unit centers for the RBF neural network. A feature selection technique based on the class separability index and correlation coefficient has been employed to identify the inputs for the RBF network. The effectiveness of the proposed approach has been demonstrated on IEEE 30-bus system and a practical 75-bus Indian system for voltage contingency screening/ranking at different loading conditions.     

基于最小径向基函数(MRBF)神经网络的非线性噪声对消

由于传统线性噪声对消器对非线性噪声不能很好的适应,本文改进传统线形噪声对消器的线形滤波器部分,用MRBF网络代替横向LMS结构,以适应非线形噪声存在的情况.使用改进的RBF网络结构MRBF[1],解决了隐单元数目确定的困难,学习算法采用更加稳定的扩展卡尔曼滤波方法(KEF),增强了RBF的实用性.本文提出的方法降低了运算复杂度,增强实时性.仿真结果表明该方法有良好的噪声对消性能.     

基于混合粒子群算法和NRBF神经网络的短期电价预测

提出一种混合粒子群算法,在局部邻近区域的粒子群算法中引入收缩因子和被动聚集,将最邻近聚类用于NRBF 神经网络的参数确定中,采用混合粒子群算法优化最近邻聚类的聚类半径,从而确定NRBF 神经网络的参数,提高了NRBF 神经网络的泛化能力。以美国PJM电力市场公布的2006年负荷与电价数据进行预测验证,证明了此方法所建立的模型的合理性和有效性。     

基于混合粒子群算法和NRBF神经网络的短期电价预测

提出一种混合粒子群算法,在局部邻近区域的粒子群算法中引入收缩因子和被动聚集,将最邻近聚类用于NRBF神经网络的参数确定中,采用混合粒子群算法优化最近邻聚类的聚类半径,从而确定NRBF神经网络的参数,提高了NRBF神经网络的泛化能力.以美国PJM电力市场公布的2006年负荷与电价数据进行预测验证,证明了此方法所建立的模型的合理性和有效性.     

Fault classification and location using HS-transform and radial basis function neural network

A new approach for protection of transmission lines has been presented in this paper. The proposed technique consists of preprocessing the fault current and voltage signal sample using hyperbolic S-transform (HS-transform) to yield the change in energy and standard deviation at the appropriate window variation. After extracting these two features, a decision of fault or no-fault on any phase or multiple phases of the transmission line is detected, classified, and its distance to the relaying point found out using radial basis function neural network (RBFNN) with recursive least square (RLS) algorithm. The ground detection is done by a proposed indicator ‘index’. As HS-transform is very less sensitive to noise compared to wavelet transform, the proposed method provides very accurate and robust relaying scheme for distance protection.     

Accurate fault locator for EHV transmission lines based on radial basis function neural networks

This paper describes the design and implementation of an artificial neural networks-based fault locator for extra high voltage (EHV) transmission lines. This locator utilizes faulted voltage and current waveforms at one end of the line only. The radial basis function (RBF) networks are trained with data under a variety of fault conditions and used for fault type classification and fault location on the transmission line. The results obtained from testing of RBF networks with simulated fault data and recorded data from a 400 kV system clearly show that this technique is highly robust and very accurate. The technique takes into account all the practical limitations associated with a real system. Thereby making it possible to effectively implement an artificial intelligence (AI) based fault locator on a real system.