Detection of loss of grid event in distributed generation systems using pattern recognition

使用模式识别检测分布式发电系统中的电网丢失事件

• 批准号: EP/J017116/1
• 负责人: David Atkinson
• 金额: $ 30.39万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ017116%2F1
• 关键词: Detection; loss; grid; event; distributed

There is an urgent need to expand the use of renewable energy generation systems to meet UK government targets. The expansion of grid-connected renewable energy sources must be done in a way which does not reduce the security of the power distribution system. Integral to power distribution system security is the ability of distributed generators to reliably detect a loss of grid condition. This is important to prevent unwanted islanded sections of the system which continue to be energised after the grid connection is lost. An islanding system occurs when a part of the grid system become disconnected from the rest of the network but continues to be energised by localised distributed generation systems. Islanded systems are (i) potentially hazardous to power system workers, (ii) may operate outside voltage and frequency tolerance, (iii) may be inadequately grounded and (iv) may not re-synchronise properly leading to undesirable protection trips. The existing approaches to islanding detection either have operational regions in which they fail to work or are required to have artificial signals injected into the grid which can impact on power quality. The most difficult operational condition is when there is a power balance between a distributed generator and its local load network. During this condition many systems will not detect that the grid connection has been lost because the fundamental frequency quantities are not altered by the event.Many renewable generation systems require a grid-connected inverter to transfer power into the grid. This is necessary because the generating source itself is rarely capable of producing power at grid frequency. A grid-connected inverter must be able to detect the loss of grid event. This proposal will investigate a novel approach using pattern recognition with a high sampling rate. The pattern recognition system is required to analyse the inverter output voltages and currents to determine if a loss of grid event has taken place. The novelty in this proposal is to include the high frequency information due to PWM effects in the real-time analysis. The benefit of doing this is that information will still be available to the pattern recognition system when a power balance condition exists.There is concern that many islanded detection systems are not immune from the effects of other neighbouring equipment. This equipment may be power electronic loads or other grid-connected inverters. The basis of the proposed approach is that the pattern recognition system will be able to discriminate between the presence and absence of the grid connection despite potential interference signals from neighbouring equipment. A major advantage of the proposed scheme is that it makes use of high frequency signals generated by the PWM switching in the inverter. These signals are extracted from the output voltages and currents by using high sampling rates where a number of samples are taken during one PWM cycle. These signals will still be present when a balance load condition exits and therefore will provide valuable diagnostic information during this difficult condition. These high frequency signals will add to the signals associated with the fundamental frequency components to detect the loss of grid event.It is important that this scheme is demonstrated experimentally and therefore a test rig will be produced which contains a range of typical loads and other grid-connected generators. The rig will be used to evaluate the performance of the proposed scheme in the presence of other neighbouring power electronic equipment.If successful the research will have major impact on the integration of renewable energy generation into power distribution systems. There will be significant benefits to power distribution system security and to the manufacturers of grid-connected inverters.

迫切需要扩大可再生能源发电系统的使用，以实现英国政府的目标。并网可再生能源的扩张必须以不降低配电系统安全性的方式进行。配电系统安全的一个重要组成部分是分布式发电机能够可靠地检测电网断电状况。这对于防止系统中不需要的孤岛部分在电网连接丢失后继续通电非常重要。当电网系统的一部分与网络的其余部分断开但继续由局部分布式发电系统供电时，就会出现孤岛系统。孤岛系统 (i) 对电力系统工作人员具有潜在危险，(ii) 可能在电压和频率容限范围之外运行，(iii) 可能接地不充分，以及 (iv) 可能无法正确重新同步，从而导致意外的保护跳闸。现有的孤岛检测方法要么具有无法工作的操作区域，要么需要将人工信号注入电网，这可能会影响电能质量。最困难的运行条件是分布式发电机与其本地负载网络之间存在功率平衡。在这种情况下，许多系统不会检测到电网连接已丢失，因为基频量不会因该事件而改变。许多可再生能源发电系统需要并网逆变器将电力传输到电网中。这是必要的，因为发电源本身很少能够以电网频率发电。并网逆变器必须能够检测电网断电事件。该提案将研究一种使用高采样率模式识别的新方法。模式识别系统需要分析逆变器输出电压和电流，以确定是否发生了电网损失事件。该提案的新颖之处在于在实时分析中包含了由于 PWM 效应而产生的高频信息。这样做的好处是，当存在功率平衡条件时，模式识别系统仍然可以使用信息。有人担心，许多孤岛检测系统无法免受其他相邻设备的影响。该设备可能是电力电子负载或其他并网逆变器。所提出方法的基础是，尽管来自相邻设备的潜在干扰信号，模式识别系统仍能够区分电网连接的存在和不存在。该方案的一个主要优点是它利用了逆变器中 PWM 开关产生的高频信号。这些信号是通过使用高采样率从输出电压和电流中提取的，其中在一个 PWM 周期内采集大量样本。当平衡负载条件存在时，这些信号仍然存在，因此将在这种困难条件下提供有价值的诊断信息。这些高频信号将添加到与基频分量相关的信号中，以检测电网事件的损失。重要的是，该方案通过实验进行论证，因此将生产一个包含一系列典型负载和其他并网发电机的测试装置。该设备将用于在存在其他邻近电力电子设备的情况下评估所提出方案的性能。如果成功，该研究将对可再生能源发电并入配电系统产生重大影响。这将为配电系统安全和并网逆变器制造商带来显着的好处。

项目成果

Impact of grid background harmonics on inverter-based islanding detection algorithms

电网背景谐波对基于逆变器的孤岛检测算法的影响

• DOI: 10.1109/peds.2015.7203480
• 发表时间: 2015
• 作者: Elgendy M

Assessment of the Incremental Conductance Maximum Power Point Tracking Algorithm

• DOI: 10.1109/tste.2012.2202698
• 发表时间: 2013-01-01
• 期刊: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
• 影响因子: 8.8
• 作者: Elgendy, Mohammed A.;Zahawi, Bashar;Atkinson, David J.
• 通讯作者: Atkinson, David J.

Evaluation of incremental conductance MPPT algorithm at low perturbation rates

• DOI: 10.1049/cp.2014.0429
• 发表时间: 2014
• 作者: M. Elgendy;B. Zahawi;D. Atkinson

Experimental investigation of the incremental conductance maximum power point tracking algorithm at high perturbation rates

• DOI: 10.1049/iet-rpg.2015.0132
• 发表时间: 2016-02-01
• 期刊: IET RENEWABLE POWER GENERATION
• 影响因子: 2.6
• 作者: Elgendy, Mohammed Ali;Atkinson, David John;Zahawi, Bashar
• 通讯作者: Zahawi, Bashar