Power system transient stability assessment based on the multiple paralleled convolutional neural network and gated recurrent unit

In order to accurately evaluate power system stability in a timely manner after faults, and further improve the feature extraction ability of the model, this paper presents an improved transient stability assessment (TSA) method of CNN?+?GRU. This comprises a convolutional neural network (CNN) and gated recurrent unit (GRU). CNN has the feature extraction capability for a micro short-term time sequence, while GRU can extract characteristics contained in a macro long-term time sequence. The two are integrated to comprehensively extract the high-order features that are contained in a transient process. To overcome the difficulty of sample misclassification, a multiple parallel (MP) CNN?+?GRU, with multiple CNN?+?GRU connected in parallel, is created. Additionally, an improved focal loss (FL) function which can implement self-adaptive adjustment according to the neural network training is introduced to guide model training. Finally, the proposed methods are verified on the IEEE 39 and 145-bus systems. The simulation results indicate that the proposed methods have better TSA performance than other existing methods.     

Power maximization control of variable‐speed wind generation system using permanent magnet synchronous generator

This paper proposes sensorless output power maximization control of a wind generation system. A permanent magnet synchronous generator (PMSG) is used as a variable speed generator in the proposed system. The generator torque is suitably controlled according to the generator speed and thus the power from a wind turbine settles down on the maximum power point by the proposed MPPT control method, where the information on wind velocity is not required. Moreover, the maximum available generated power is obtained by the optimum current vector control. The current vector of PMSG is optimally controlled according to the generator speed and the required torque in order to minimize the losses of PMSG considering the voltage and current constraints. The proposed wind power generation system can be achieved without mechanical sensors such as wind velocity detector and a position sensor. Several experimental results show the effectiveness of the proposed control method. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(2): 11–19, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20043     

分布式光伏灵活并网集群调控技术综述

分布式光伏发电单体可控性差且波动性强,其大规模汇集极大地影响了电网的安全稳定运行.文中从分布式光伏发电系统的集群划分和运行控制两个方面介绍了国内外对光伏发电研究的新趋势,重点评述了集群划分标准的异同和划分算法的优劣,并从三个方面论述了集群控制的概念和方法.     

线性自抗扰在光伏发电系统MPPT中的应用

为了保证光伏发电系统能够快速地达到最大功率点并且能够稳定运行,设计了一种基于线性自抗扰控制(LADRC)的最大功率点跟踪(MPPT)控制器。建立了光伏电池与DC-DC变换器相结合的数学模型。设计了1阶LADRC对其进行解耦控制,目的是能够快速地确定光伏电池阵列的最优输出电压,减少功率振荡。分析了MPPT控制器的设计原理。为了验证所设计的MPPT控制器的实效性与普适性,通过Matlab/Simulink进行了仿真研究。仿真实验表明,加入所设计的MPPT控制器,能够明显提高算法的跟踪速度,大大降低功率振荡,即使在外界环境变化剧烈的情况下,也能表现出良好的控制能力,通用性很强。     

适应分布式光伏动态特性的改进电能计量模型

针对分布式光伏发电模型,建立了分布式光伏并网信号数学表征式;基于时分割乘法器(TDM)电能计量模型,分析电能计量算法在谐波和电压波动条件下的误差产生理论,设计适应分布式光伏动态特性的有功计量修正模块,给出基于Hilbert变换的无功功率计量方法;将光伏并网信号数学表征式与改进的电能计量模型结合,提出了适应分布式光伏动态特性的改进电能计量模型,通过仿真验证了所提模型的有效性。     

电导增量法光伏发电MPPT研究综述

综述了在单峰值的情况下光伏发电系统中最大功率点跟踪使用的电导增量法以及各种变体算法,总结分析各自的优缺点,对光伏阵列最大功率点跟踪技术的跟踪速度、控制精度及将来研究和应用中需要解决的问题作了探讨。     

利用光伏发电优化功率控制提高电网暂态稳定

随着光伏发电装机容量的逐步提高,光伏高渗透率的区域电网的稳定性面临挑战。因此进行了光伏电站的功率控制研究,在故障暂态过程中,合理减少光伏系统有功输出,保证光伏直流侧稳定,提高光伏逆变器的无功输出能力,以实现电网电压稳定和网内机组功角特性的改善。以美国西部联合电力系统(WSCC)3机9节点系统接入光伏电站为例,验证了所提控制策略能有效提高电网内机组的暂态稳定能力,有利于光伏发电的安全并网,并改善了输出性能。     

双模糊控制法在光伏并网发电系统MPPT中的应用

提出一种在光伏并网发电系统中进行最大功率点跟踪(MPPT)的双模糊控制法,将非对称模糊MPPT与模糊PID相结合,在设定参考电压环节使用模糊控制代替诸如扰动观察法等传统方法,在消除实际电压与参考电压偏差这一环节用模糊PID替换普通的PID控制。此外,还提出了4个反映MPPT性能的指标:环境缓慢变化时的MPPT时间、光伏阵列发出的能量大小、稳态时的功率波动大小和环境剧烈变化时光伏阵列发出的能量大小。设计了4个算例,在MATLAB/Simulink环境下对5种控制方法分别进行了仿真分析。通过对比各方法的性能指标和相应的输出功率波形图,验证了所提出的双模糊控制法是一种比传统方法更优的MPPT控制方法。     

光伏发电系统MPPT控制方法的研究及改进

最大功率跟踪（MPPT）技术是光伏系统中经常使用的跟踪技术,但在使用中存在一定的缺陷和不足之处,如跟踪速度慢和振荡。鉴于这些问题,在此提出了一种结合型的MPPT控制方法,该方法在分析了扰动观察法的优势和不足以及概述了滞环比较法原理的基础上,将扰动观察法的跟踪优势与滞环比较法的滞环原理相结合,实现了系统控制方法的优化。并通过与传统的控制方法的仿真图进行对比,通过对比得出该改进方法能快速跟踪到最大功率点及有效减小振荡,验证了该方法的正确性和有效性。     

基于预测控制的DFIG电网无功补偿优化方案

提出一种基于预测控制的DFIG功率调节控制方案,增强DFIG调节电网电能质量的能力,提高DFIG系统的无功输出能力。首先比较了DFIG系统和统一电能质量调节器的控制方法。针对DFIG功率分配动态多变量的过程,设计三输入三输出的广义预测控制方案,通过模型预测、滚动优化和反馈校正,提高了DFIG的无功补偿能力。仿真对比了多种情况下最大功率点跟踪控制和广义预测控制下的DFIG的无功输出,证明了使用广义预测控制可以有效的提高DFIG的无功补偿总量,增强DFIG系统调节电网电能质量的能力。     

Optimization of distributed generation systems using a new discrete PSO and OPF

Many methods have been applied to achieve optimal site and size of distributed generation systems. This paper introduces a new hybrid method, which employs discrete particle swarm optimization and optimal power flow to overcome this shortcoming. The main technical constraints are imposed for utilities, which could apply this approach to search the best sites to connect distributed generation systems in a distribution network choosing among a large number of potential combinations. A fair comparison between the proposed algorithm and other methods is performed. For such goal, convergence curves of objective function versus number of iterations are computed. The proposed algorithm reaches a better solution than Genetic Algorithms considering similar number of evaluations.     

基于改进的扰动观察法在光伏发电MPPT中的应用

光伏电池是一种非线性电源,随外界环境的变化而变化,为了提高光伏阵列的利用率,光伏系统中需采用最大功率跟踪(maximum power point tracking,MPPT)。近年的研究中,提出了许多跟踪算法,其中应用最为广泛的是扰动观察法和电导增量法。在分析扰动观察法的基础上,进行优化提出了一种改进的变步长算法,它有效提高了最大功率点跟踪过程中的跟踪速率,克服了扰动方向的误判问题,消除了在最大功率点的振荡现象。仿真与实验结果证明了该方法的有效性。     

Optimization of biomass fuelled systems for distributed power generation using Particle Swarm Optimization

With sufficient territory and abundant biomass resources Spain appears to have suitable conditions to develop biomass utilization technologies. As an important decentralized power technology, biomass gasification and power generation has a potential market in making use of biomass wastes. This paper addresses biomass fuelled generation of electricity in the specific aspect of finding the best location and the supply area of the electric generation plant for three alternative technologies (gas motor, gas turbine and fuel cell-microturbine hybrid power cycle), taking into account the variables involved in the problem, such as the local distribution of biomass resources, transportation costs, distance to existing electric lines, etc. For each technology, not only optimal location and supply area of the biomass plant, but also net present value and generated electric power are determined by an own binary variant of Particle Swarm Optimization (PSO). According to the values derived from the optimization algorithm, the most profitable technology can be chosen. Computer simulations show the good performance of the proposed binary PSO algorithm to optimize biomass fuelled systems for distributed power generation.     

Power factor control for inverters under high penetration of distributed generation

The paper proposes a method to select a power factor for an inverter of distributed power generation systems to keep appropriate voltage of a power system with the high penetration of inverter-interfaced generation. The increase of inverter-interfaced power sources, such as wind and photovoltaic power generation, arouses concerns on the voltage fluctuations in a power system. While constant power factor control of an inverter has been studied as a means to stabilize the voltage of a power system, there still remains room to improve the control. The proposed method in the paper selects the power factor based on the R/X ratio of the looking-back impedance into the interconnected power system. By normalizing the related variables, a general formula is derived for the power factor, and the resultant relationship between the apparent power and voltage of an inverter becomes identical irrespective of the R/X ratio with the control. Sample studies show that, by operating an inverter with the power factor, voltage fluctuations are effectively suppressed even for a power system with the high penetration of inverter-interfaced generation.     

基于CSA-FPI算法的光伏发电MPPT仿真研究

对光伏阵列进行最大功率点跟踪控制(Maximum Power Point Tracking,简称MPPT),是提高光伏发电系统输出功率的有效措施之一。文章以光伏阵列非线性输出特性为切入点展开研究,在分析了常规算法的优缺点基础上,针对其在最大功率点处(MPP)动态和稳态性能不佳等问题,提出了一种基于布谷鸟搜索算法(CSA)和模糊PI(FPI)控制相结合的光伏阵列MPPT算法。在MATLAB/Simulink下进行了仿真建模,仿真结果表明该方法能够迅速准确地跟踪光伏阵列的最大功率点,防止算法跟踪方向误判情况的发生,具有快速跟踪性和鲁棒性;同时实验结果也证实了上述算法的正确性和有效性。     

Direct power control of three-phase VSIs for the minimization of common-mode emissions in distributed generation systems

This paper presents two new direct power control (DPC) strategies of three-phase grid connected VSIs for distributed generation, devised for the minimization of common-mode emissions. These strategies have been called DPC-EMC 1 (electro magnetically compatible) and DPC-EMC 2. Both of them reduce the common-mode emissions of the VSI by using either even or odd voltage vectors in each of the six sectors in which the grid voltage lies, without using any null vector. DPC-EMC 2 outperforms DPC-EMC 1 in terms ripple of the active and reactive power waveforms. These approaches permit the common-mode emissions to be reduced in comparison with the classic DPC algorithm, at the expense of an increase of the harmonic content of the injected current waveform which can be further corrected by a proper power line filter. An experimental comparison among the classic DPC, DPC-EMC 1 and DPC-EMC 2 is presented in terms of dynamic performance, harmonic content of the injected current and harmonic content of the common-mode voltage.     

Application of a simplified Grey Wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system

A Simplified Grey Wolf Optimizer (SGWO) is suggested for resolving optimization tasks. The simplification in the original Grey Wolf Optimizer (GWO) method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process. The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal, multimodal, and fixed dimension test functions. The results are also contrasted to the Gravitational Search Algorithm, the Particle Swarm Optimization, and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique. Practical application in a Distributed Power Generation System (DPGS) with energy storage is then considered by designing an Adaptive Fuzzy PID (AFPID) controller using the suggested SGWO method for frequency control. The DPGS contains renewable generation such as photovoltaic, wind, and storage elements such as battery and flywheel, in addition to plug-in electric vehicles. It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task. It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller. A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance. Finally, the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.     

Distortions identification and compensation based on artificial neural networks using symmetrical components of the voltages and the currents

The problem of harmonics identifying and compensating has been of great interest in recent years. A new neural identification scheme for an active power filter (APF) is proposed. This scheme identifies the direct, inverse and zero sequence components of both the voltages and the currents of the power network. The components result from a new and generic decomposition of a three-phase signal which can be either the voltage or the current. For one signal, the direct components extraction requires two independent Adaline networks, and the inverse components extraction two other Adalines. The voltage and current components are used to on-line compute the instantaneous direct, inverse and zero sequence powers. The proposed decomposition is a new formulation of the instantaneous powers and is also appropriate for unbalanced systems. The reference compensation currents can be determined according to different compensation objectives. The resulting compensation currents are then re-injected phase-opposite through the APF in real-time. The performance is evaluated through several simulation examples and through different experiments. The results show that the proposed neural method outperforms other methods, such as the conventional instantaneous power theory.     

计及分布式电源局部调压能力的10 kV配电网低电压治理方法

为了充分利用分布式电源局部调压能力治理10 kV配电网的低电压问题,提出了一种利用分布式电源改善电压的方法。根据配电网5种典型运行案例的节点电压,利用模糊聚类分析对配电网中节点进行二次分区,并利用改进的粒子群算法在分区内优化配置分布式电源和无功补偿设备。计及分布式电源和无功补偿设备的输出电流以及有载调压变压器变比的影响,推导出了计算节点电压变化量的公式以及基于电流的电压灵敏度分析矩阵,并提出了基于灵敏度分析矩阵的电压控制策略。以存在"低电压"问题10 kV配电网为例,对所提出的方法进行了分析和验证,结果表明了所提方法的可行性和有效性。     

变速恒频双馈风力发电系统最大功率跟踪算法的比较与仿真研究

研究比较了最大风能捕获的3种算法:直接转速控制法、最佳功率一转速曲线跟踪法、爬山搜索法,从稳定性、可控性方面进行研究比较分析,并用所搭建的系统模型进行详细的仿真研究,仿真结果验证了基于最佳功率一转速曲线跟踪法的变速恒频双馈风力发电系统能够快速地进行最大功率点跟踪(MPPT),从而最大效率地利用风能.