含海洋能发电的海岛微网能量优化调度方法

针对海岛微电网分布式电源和负荷的独有特性,以海岛系统运行经济性为目标,考虑新能源消纳因素,提出一种海岛微电网能量优化调度方法。首先在分析典型海洋能发电出力特性的基础上,建立了含风、光、柴、储、波浪能、潮汐能以及可控负荷的海岛微网能量优化调度模型;而后面向日前和日内2个时间尺度,采用CPLEX完成混合整数规划,利用日前调度计划与日内实时滚动推演结果比对,依据日内滚动计算结果修正日前调度计划,实现当日微网能量调度全局最优。算例分析表明,由于利用了日前和日内相结合的滚动计算方法,使对岛上源、荷的预测精度逐级提高,该优化调度方法在兼顾海岛微电网功率平衡和可再生能源利用率的同时,最大限度降低了海岛微电网运维成本。     

基于线路阻抗短时调节的多直流连锁换相失败抑制方法

连锁换相失败是交直流混联电网的新型连锁故障形式之一。结合连锁关系在换相失败相关现象中的体现和参与环节,文中提出了一种在关键线路短时调节阻抗以降低耦合的抑制策略,重点讨论了策略的实施位置。在加权网络模型的基础上,基于直流分群界定了换相失败的连锁范围。在直流群内搜索对直流耦合关系影响较大的关键路径,并采用潮流介数比较各线路阻抗变化对系统运行状态的冲击,以避免在重要线路采取措施。针对不同的扰动,利用换相电压时间面积判断需要启动策略的直流群,从而实现有差别的连锁换相失败抑制。在修改的IEEE 39节点系统和某省市电网算例中,利用PSCAD/EMTDC开展仿真,结果均验证了关键线路阻抗短时调节策略抑制连锁换相失败的效率和有效性。     

基于改进图卷积网络的配电网状态估计方法

采用小样本量测信息进行配电网实时状态估计,对提高配电网可靠性,保证其稳定运行具有重要作用。为了在配电网量测信息不足条件下进行高精度状态估计,提出了一种基于改进的图卷积网络(graph convolutional network,GCN)的物理–数据融合配电网状态估计新方法。该方法首先利用少量相量量测单元将配电网进行切割分区,然后根据分区后的最大直径确定卷积网络所需要的卷积模块数量,其次修改了传统GCN中的邻接矩阵表示方法,从而实现利用卷积网络将配电网分区子系统中的状态变量均由量测量表示。通过IEEE33节点典型算例,验证了所提方法有效性。同时,通过与传统的高斯–牛顿优化算法和传统深度学习网络对比测试,结果表明,所提方法不仅能够将计算复杂度转移到离线阶段,而且能够不依赖于高冗余度的伪量测,具有较高的估计精度与计算速度。     

基于智能软开关的三相不平衡配电网动态重构策略

针对单相分布式电源（DG）的接入会加剧配电网不平衡程度、增大网络损耗,且在严重不平衡时影响系统安全运行的问题,提出计及智能软开关（SOP）的三相不平衡配电网动态重构策略。首先,构建考虑SOP和DG电流不平衡度约束的三相不平衡配电网动态重构模型;然后,针对模型的非凸性将原模型转化为混合整数线性规划模型;最后,对改进的IEEE34节点配电网和某地78节点实际配电网进行算例分析,结果表明所提模型和策略可在保证配电网的安全运行的同时提升配电网的经济效益。     

计及分时电价的5 G基站光储系统容量优化配置方法

随着第5代移动通信技术（5G通信）的迅速发展,5G基站数量不断增加,5G通信耗电量大、用电成本高的问题日益突出。为此,提出了考虑光伏和储能接入的5G基站光储系统优化配置方法,以提高5G基站运行的经济性。首先,考虑5G基站负荷情况和配电网分时电价,建立了5G基站光储系统的经济调度模型;然后,通过量子粒子群优化算法计算典型日内5G基站光储系统的最小综合成本,以此确定光伏和储能的最优接入容量;最后,通过算例证明合理配置光伏和储能容量可以提高5G基站系统的经济性。光伏和储能容量的配置受储能成本和峰谷电价差的影响较大,且光储系统所能带来的经济效益随峰谷电价差的增大和储能成本的降低而增大。     

Neural network-based sliding mode adaptive control for robot manipulators

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.     

Arc characteristics and a single-pole auto-reclosure scheme for Alexandria HV transmission system

Arcing faults are the most common faults that occur in the 220 kV transmission line national grid of Egypt, in the vicinity of ‘Alexandria’ city. This part of the network also feeds a large industrial load in the form of steel works, which injects a large amount of harmonics into the network. In this paper, the performance of this 220 kV TL network during arcing faults is studied with the aid of the PSCAD/EMTDC program together with a specially designed arcing fault custom model incorporated in the program. Several arcing faults are simulated, and the effect of harmonics on the fault current and secondary arc duration time is demonstrated. Based upon the simulation results, recommendations regarding single-pole autoreclosing and arc suppression methods are suggested.     

Decision trees aided scheduling for firm power capacity provision by virtual power plants

The incorporation of Distributed Generation (DG) under the Virtual Power Plant (VPP) concept allows the market integration of several and largely dispersed electric power sources. One matter of concern for the VPP owner and operator is to follow the hourly schedule regardless of the stochastic nature of some of its sources or any unpredicted generation outages. This study presents a Decision Tree (DT) based methodology that prepares for the dispatching of power equivalent to the possible loss of the highest injection of one of the sources of the VPP (according to day-ahead hourly schedule) to the rest of its sources, on an hour-ahead horizon. This allows VPP operators to provide firm capacity and participate effectively in the energy market.     

Data driven approach for fault detection and diagnosis of turbine in thermal power plant using Independent Component Analysis (ICA)

In this paper, a statistical signal processing technique, known as Independent Component Analysis (ICA) for fault detection and diagnosis in the real Turbine system (V94.2 model) is suggested. The information of one of MAPNA’s power plants turbine system is utilized at first. In order to reduce the dimensionality of the data set, to identify the essential variables and to choose the most useful variables, PCA approach is applied. Then, the fault sources are diagnosed by ICA technique. The results indicate that suggested approach can distinguish main factors of abnormality, among many diverse parts of a typical turbine system. The presented results will show that suggested approach can avoid false alarms and fault misdiagnosis due to changes in operation conditions and model uncertainty. The presented results show the validity and effectiveness of ICA approach for faults detection and diagnosis in noisy states.     

Stochastic frequency-security constrained energy and reserve management of an inverter interfaced islanded microgrid considering demand response programs

This paper addresses a novel security constrained energy management system of a microgrid which considers the steady-state frequency. Microgrid frequency as a key control variable, continuously exposes to be excursed of its nominal value due to unpredictable intermittencies arise from renewable sources and/or load consumptions. Moreover, great utilization of inertia-less inverter-interfaced distributed energy resources intensifies potential frequency excursions. As a result, energy and reserve resources of a microgrid should be managed such that the microgrid frequency lies within secure margins. To that end, a new objective function on the basis of the frequency dependent behavior of droop-controlled distributed generations is formulated using a mixed integer linear programming. It is aimed to optimize the microgrid frequency according to the economic and environmental policies. Besides, to seek the active participation of the consumers into proposed frequency management approach, a linearized ancillary service demand response program is also proposed. In addition, to properly model the impacts of microgrid various uncertainties in the frequency management approach, a two-stage stochastic optimization algorithm is employed. Simulations are performed in a typical microgrid which operates in the islanded mode during a 24 h scheduling time horizon. The numerical results show the impressiveness of the proposed frequency aware energy management system while concurrently managing the microgrid security and economical aspects. Furthermore, it is demonstrated that utilization of demand response programs economizes the microgrid frequency management approach.