Islanding detection of distributed generation by using multi-gene genetic programming based classifier

This paper proposed a new method for detecting islanding of distributed generation (DG), using Multi-gene Genetic Programming (MGP). Islanding has been a serious concern among power distribution utilities and distributed generation owners, because it poses risks to the safety of utilities’ workers and consumers, and can cause damage to power distribution systems’ equipment. Therefore, a DG must be disconnected as soon as an islanding is detected. In addition, an islanding detection method must have high degree of dependability to correctly discriminate islanding from other events, such as load switching, in order to avoid unnecessary disconnection of the distributed generator. In this context, the novelty of the proposed method is that the MGP is capable of obtaining a set of mathematical and logic functions employed to detect and classify islanding correctly. This is a new approach among the computational intelligent methods proposed for DG islanding detection. The main idea was to use local voltage measurements as input of the method, eliminating the need of complex and expensive communication infrastructure. The method has been trained with several islanding and non-islanding cases, by using a power distribution system comprising five concentrated loads, a synchronous distributed generator and a wind power plant. The results showed that the proposed method was successful in differentiating the islanding events from other disturbances, revealing its great potential to be applied in anti-islanding protection schemes for distributed generation.     

基于自治单元的配电网可靠性分析及优化配置

本文重点分析接入分布式电源的配电网可靠性,并基于可靠性指标建立优化配置模型,解决新能源大量接入环境下配电网存在的可靠性分析困难的问题。本文根据接入分布式电源后电网的特性,将配电网简化为以自治单元为主体的系统,然后进行自治分区划分并分类,计算不同类别自治分区的可靠性指标。并且通过可靠性指标建立优化模型,最终对系统中分布式电源的容量进行配置。通过实际算例分析,本文提出的方法能够正确的计算系统的可靠性指标,并且将负荷的可靠性指标用自治区域指标替代,从计算量上减小计算的复杂度,比传统方法具有更快的计算速度。因此基于自治单元的配电网可靠性分析和优化配置方法更适用于当前大规模接入分布式电源的系统,为解决目前配电网可靠性计算的难题提供了有效手段。     

Demand response scheduling to support distribution networks operation using rolling multi-period optimization

This paper presents an approach of demand response (DR) scheduling with thermostatically controlled loads (TCL) in a distribution grid with high penetration of distributed generations (DG). In this approach, household TCL are employed as flexibility resources to support the distribution network for mitigating voltage or congestion constraints. A two-stage rolling optimization based control scheme is proposed to determine the optimal operating status of flexible loads using the forecast of generation and demand in the distribution system. The proposed methodology is conducted in a distribution test feeder with realistic scenarios. The simulation results have shown the usefulness and efficiency of the proposed method in improving the network operation and increasing the hosting capacity of DG.     

Impact of wind farms on disturbance detection and classification in distributed generation using modified Adaline network and an adaptive neuro-fuzzy information system

A new disturbance detection and classification technique based on modified Adaline and adaptive neuro-fuzzy information system (ANFIS) is proposed for a distributed generation system comprising a wind power generating system (DFIG) and a photovoltaic array. The proposed technique is based on a fast Gauss–Newton parameter updating rule rather than the conventional Widrow–Hoff delta rule for the Adaline network. The voltage and current signals near the target distributed generation (DG), particularly the DFIG, whose speed varies from minimum to the maximum cut-off speed, are processed through the modified Adaline network to yield the features like the negative sequence power, harmonic amplification factor (HAF), total harmonic distortion (THD), etc. These features are then used as training sets for the ANFIS, which employs a gradient descent algorithm to update its parameters. The proposed technique distinguishes the islanding condition of the distributed generation system with some other disturbances, such as switching faults, capacitor bank switching, voltage swell, voltage sag, distorted grid voltage, unbalanced load switching, etc. which are referred to as non-islanding cases in this paper.     

多源多变换微电网系统建模与动态稳定分析方法

微电网内部电力电子变换器接口型分布式电源广泛存在.电力电子接口微源具备与传统电力系统电源拓扑结构、控制方法和动态特性的差异性,控制方法的多样性、电力电子接口微源高渗透率将给低惯量微电网的协调控制和安全稳定运行带来严峻的挑战.多类型微源、多类型负荷在微电网内混合共存,可能引发源源耦合交互、负荷间交互以及源荷交互,不同特性的设备间相互作用将重新塑造区别与传统电力系统的动态响应特性,并诱发稳定性问题.本文首先归纳总结可再生能源渗透率不断提升下多源多变换微电网典型运行特性和存在的稳定性问题,详细阐述了含风光柴储多源多变换接口的微电网模块化动态建模方法.在此基础上,给出了用于微电网动态稳定分析的特征值分析方法和基本步骤.以珠海东澳岛多源多变换智能微电网为对象,基于提出的动态建模方法和动态稳定分析方法,给出建模与分析讨论结果.论文对多源多变换微电网系统动态稳定分析方法的论述可为微电网的广泛推广和应用提供建模和分析理论基础.     

A probabilistic model for assessing the reliability of wind farms in a power system

Modeling the generation of a wind farm and its effect on power system reliability is a challenging task, largely due to the random behavior of the output power. In this paper, we propose a new probabilistic model for assessing the reliability of wind farms in a power system at hierarchical level II (HLII), using a Monte Carlo simulation. The proposed model shows the effect of correlation between wind and load on reliability calculation. It can also be used for identifying the priority of various points of the network for installing new wind farms, to promote the reliability of the whole system. A simple grid at hierarchical level I (HLI) and a network in the north-eastern region of Iran are studied. Simulation results showed that the correlation between wind and load significantly affects the reliability.     

Incomplete information-based decentralized cooperative control strategy for distributed energy resources of VSI-based microgrids

This paper presents an effective method to control distributed energy resources (DERs) installed in a microgrid (MG) to guarantee its stability after islanding occurrence. Considering voltage and frequency variations after islanding occurrence and based on stability criteria, MG pre-islanding conditions are divided into secure and insecure classes. It is shown that insecure MG can become secure, if appropriate preventive control is applied on the DERs in different operating conditions of the MG. To select the most important variables of MG, which can estimate proper values of output power set points of DERs, a feature selection procedure known as symmetrical uncertainty is used in this paper. Among all the MG variables, critical ones are selected to calculate the appropriate output power of different DERs for different conditions of the MG. The values of selected features are transmitted by the communication system to the control unit installed on each DER to control its output power set point. In order to decrease the communication system cost, previous researchers have used local variables to control the set point of different DERs. This approach decreases the accuracy of the controller because the controller uses incomplete information. In this paper, multi-objective approach is used in order to decrease the cost of the communication system, while keeping the accuracy of the preventive control strategy in an allowable margin. The results demonstrate the effectiveness of the proposed method in comparison with other methods.     

含混合储能微网对配电网可靠性的影响

混合储能可快速平滑微网中分布式电源出力的随机性,从而提升配电网可靠性。提出了一种基于网络划分的蒙特卡洛模拟法,定量研究了含混合储能微网对配电网可靠性的影响。首先建立蓄电池与超级电容组成的混合储能系统模型并制定其充放电策略;在设备发生故障后,根据系统区域划分和负荷削减模型,确定负荷停电原则,采用序贯蒙特卡洛模拟法计算可靠性指标。以改进的IEEE RBTS BUS6系统为算例,对比分析了不同储能方式的微网接入前后的系统可靠性指标,结果表明：含混合储能微网接入配电网,能提高配电网供电的可靠性。     

分布式电源对配网的影响分析研究综述

随着传统化石能源的日渐枯竭,新能源需求十分迫切。基于新能源及可再生能源的分布式电源（DG）不仅可以作为传统供电模式的重要补充,还将在能源综合利用上占有重要地位。大力发展以以分布式利用为主的新能源,符合国家能源战略。但随着DG在电力系统中所占比例越来越大,对传统的电力系统安全稳定提出了挑战。为研究分布式电源并网对配网影响及改进措施,文中介绍了分布式电源的概念、分类及特点,论述了DG并网对配网的影响及改进的研究现状。从分布式电源并网后对配网规划、电能质量、继电保护等方面总结了DG并网对配网的影响及其改进措施。论述了含DG的配网故障后的孤岛问题及微网的相关研究。展望了未来DG在电网中高比例发展高情况下的研究方向     

单三相光储型多微网黑启动恢复策略

近年来微电网大力发展,由其为子单元的多微网(multi-microgrids,MMGs)渐成为一种新型电网.作为结构及控制模式更加复杂的微电网群落,研究安全有效地黑启动以进入正常运行状态对提高供电可靠性具有重要意义.在分析含单三相复合混联的多微网结构及控制系统基础上,为使主电源高效运行,提出一种考虑三相不平衡度约束的多微电网黑启动策略,考虑源荷网条件选择最优的储能系统作为黑启动主电源,以先独立分散恢复再集中协调恢复的方式实现多微网的源荷网恢复.所提策略经验证有效可靠,可为多微网黑启动恢复提供一定的借鉴意义.     

A novel neural network with simple learning algorithm for islanding phenomenon detection of photovoltaic systems

This study aimed to propose an intelligent islanding phenomenon detection method for a photovoltaic power generation system. First, a PSIM software package was employed to establish a simulation environment of a grid-connected photovoltaic (PV) power generation system. A 516W PV array system formed by Kyocera KC40T photovoltaic modules was used to complete the simulation of the islanding phenomenon detection method. The proposed islanding phenomenon detection technology was based on an extension neural network (ENN), which combined the extension distance of extension theory, as well as the learning, recalling, generalization and parallel computing characteristics of a neural network (NN). The proposed extension neural network was used to distinguish whether the trouble signals at the grid power end were power quality interference or actual islanding operations, in order that the islanding phenomenon detection system could cut off the load correctly and promptly when a real islanding operation occurs. Finally, the feasibility of the proposed intelligent islanding detection technology was verified through simulation results.     

双频并网逆变器及其孤岛效应检测

提出一种用于分布式发电的双频并网逆变器,该逆变器由2个标准三相桥级联而成,其中一个桥工作在高频,提高输出电流性能;另一个桥工作在低频,主要输出功率,从而降低开关损耗,提高系统效率。为了检测孤岛效应,提出了基于dq变换和正反馈的新检测策略,基于该策略,提出了基于dq变换的电压正反馈和频率正反馈两种检测方法。两种方法具有无检测盲区、对电能质量影响小、检测速度快等优点。仿真结果表明,该逆变器输出电流与电网电压同相,总的谐波畸变率低,向电网输出的电能质量高。     

Voltage and frequency regulation in autonomous microgrids using Hybrid Big Bang-Big Crunch algorithm

This paper proposes an optimal power control strategy for inverter-based Distributed Generation (DG) units in autonomous microgrids. It consists of power, voltage, and current controllers with Proportional-Integral (PI) regulators. The droop concept is used for the power control strategy. Static parameters in PI regulators may not ensure the most optimal solution due to inevitable changes happening in microgrid configuration and loads. In the proposed method, after occurring a load change in a standalone microgrid, parameters of the PI controller are dynamically adjusted to get the most optimal operating point that satisfies objective functions. The optimization problem is formulated as a multi-objective programming with objective functions of minimizing overshoot/undershoot, settling time, rise time, and Integral Time Absolute Error (ITAE) in the output voltage. These objective functions are combined using fuzzy memberships. The Hybrid Big Bang-Big Crunch algorithm (HBB-BC) is used to solve the optimization problem. The proposed methodology is simulated on a case study and according to obtained results, the suggested tuning of PI parameters leads to a better voltage response than previous methods. The case study is also solved using the Particle Swarm Optimization (PSO) and Big Bang-Big Crunch (BB-BC) algorithms and it is found that the HBB-BC gives a better solution than the PSO and BB-BC.     

考虑状态受限和一致性的微电网二次控制

为解决交流微电网下垂控制产生的偏差问题, 本文采用二次控制对分布式电源输出电压和频率进行调节. 将微电网看成分布式多智能体系统, 智能体间通过稀疏网络进行通信, 运用多智能体一致性协议, 本文提出一种基 于障碍Lyapunov函数和自适应模糊系统的二次电压和频率控制器. 采用障碍Lyapunov函数设计控制器, 不但能保 持系统的稳定性, 还可使输出的电压和频率限制在预设的范围内. 采用自适应模糊系统可对系统中的一些参变量的 变化进行估计, 提高了控制器的鲁棒性. 本文给出了严格的稳定性证明. 通过对负载变化, 以及拓扑结构改变等的 仿真测试, 验证了所提方案的有效性.     

计及光伏及风电并网的电力系统短期负荷预测

为提升光伏、风电等分布式能源大量接入电网后短期电力负荷的预测精度,促进电网消纳能力提升,本文对光伏出力及短期用电负荷采用小波——径向基函数(RBF)神经网络预测方法;对风力发电首先利用总体平均经验模态分解(EEMD)方法对其功率数据分解,再采用BP神经网络、RBF神经网络、小波神经网络、ELMAN神经网络四种神经网络预测方法进行预测,并用粒子群算法(PSO)和灰色关联度(GRA)修正。最后,利用等效负荷的概念,分析光伏、风力发电并网对于短期电力负荷预测的影响,并将三种模型有效结合,得到了考虑光伏及风力发电并网的电力系统短期负荷预测的等效负荷预测模型。实例分析表明,本文所提方法相较于其他方法在该预测项目上具有相对更高的预测精度。     

基于JITL的电量预测高斯软测量建模研究

为了解决工业过程受本身结构特征、外界因素等影响而存在严重的非线性和时变性等问题,本文提出了一种基于输入输出综合性相似度指标的即时学习高斯过程软测量建模方法。在该方法中,将样本数据进行归一化处理,首先利用传统的基于距离和角度的相似度指标分别对样本输入输出变量进行相似度计算,进而对相似度进行综合,最后选择出最终的相关样本集,建立高斯过程回归软测量模型,将所提基于输入输出相似度指标的即时学习高斯工程软测量模型应用于城市日用电量数据的预测。研究结果表明,所提出的软测量建模方法可以实现对日用电量数据的高精度预测且预测结果具有较小的误差。因此可表明该方法可在电量预测中具有一定的应用可靠性,可以在电力市场预测分析中得到广泛的应用。     

Use of a quantile regression based echo state network ensemble for construction of prediction Intervals of gas flow in a blast furnace

The usual huge fluctuations in the blast furnace gas (BFG) generation make the scheduling of the gas system become a difficult problem. Considering that there are high level noises and outliers mixed in original industrial data, a quantile regression-based echo state network ensemble (QR-ESNE) is modeled to construct the prediction intervals (PIs) of the BFG generation. In the process of network training, a linear regression model of the output matrix is reported by the proposed quantile regression to improve the generalization ability. Then, in view of the practical demands on reliability and further improving the prediction accuracy, a bootstrap strategy based on QR-ESN is designed to construct the confidence intervals and the prediction ones via combining with the regression models of various quantiles. To verify the performance of the proposed method, the practical data coming from a steel plant are employed, and the results indicate that the proposed method exhibits high accuracy and reliability for the industrial data. Furthermore, an application software system based on the proposed method is developed and applied to the practice of this plant.     

基于模型预测控制的含多微电网的能源互联网分布式协同优化

研究了预测不确定性条件下含多个微电网的能源互联网分布式协同调度策略.各微电网都拥有多种智能负荷,如功率可调负荷、可调度负荷和关键负荷;部分微电网含有分布式电源,如微型燃气轮机、风电机组、光伏发电系统等;且部分微电网还拥有储能设备,如电池储能系统.每个微电网都可当做一个独立的实体,拥有自己的运行目标,这些运行目标可表示成混合整数规划模型.提出了基于并行分布式优化的博弈模型以较小的信息通信量协调各微电网带有竞争性的运行目标.在此基础上,引入模型预测控制（MPC）机制以降低能源互联网中风、光等可再生能源输出、负荷需求及电价波动的不确定性产生的不利影响.算例证明了本文所提方法的可行性和有效性.     

Structural reliability calculation method based on the dual neural network and direct integration method

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer–Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

     

Input–output pairing analysis for uncertain multivariable processes

Decentralized control structure is widely employed in many industrial multivariable processes. In this approach, control structure design and in particular input–output pairing is a vital stage in the design procedure. There are several powerful methods to select the appropriate input–output pair in linear multivariable plants. However, in the face of plant uncertainties, the input–output pairs can change. Input–output pairing problem, in the presence of uncertainties, and its consequences on the pairing problem have not been widely addressed. In this paper, Hankel interaction index array is used to choose the appropriate input–output pair and a new method is proposed to compute Hankel interaction index array, which reduces the computational load. Also, a theorem will be presented to show the effect of additive uncertainties on input–output pairing of the process. An upper bound on the element variations of Hankel interaction index array of the additive uncertainties in state space framework is given to show the possible change in input–output pairing. Finally, two typical processes are employed to show the main points of the proposed methodology.