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首页> 外文期刊>Electric power systems research >Neural estimation of voltage-sag waveforms of non-monitored sensitive loads at monitored locations in distribution networks considering DGs
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Neural estimation of voltage-sag waveforms of non-monitored sensitive loads at monitored locations in distribution networks considering DGs

机译：考虑DG的配电网受监控位置非受监控敏感负载电压骤降波形的神经估计

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Concerns about power quality (PQ) in distribution networks have necessitated the use of PQ measuring/monitoring equipment to detect and analyse PQ problems. As the installation of such equipment at all locations is not economically feasible, minimum number and optimum locations of PQ monitors have been sought in recent researches to meet both economical efficiency and monitoring capability. Focusing on voltage sags, this paper attempts to further reduce the number of PQ monitors obtained by optimum allocation approaches while keeping the desired monitoring capability. Growing number of electric equipment with high sensitivity to voltage sags have raised more concerns about financial losses associated with voltage sags in comparison to other PQ problems. Here, a neural estimator placed at a monitored location is proposed to estimate instantaneous voltage-sag waveforms of a non-monitored sensitive load using local measurements. Echo state network (ESN) is used as the voltage-sag waveform estimator (VSWE). Because of increasing penetration of distributed generations (DGs) and their impacts on voltage-sag performance, they are considered to challenge the estimation task. The proposed ESN-based VSWE is examined on the IEEE 37-bus network. Tests for extensive unseen cases with different fault resistances, fault inception-angles, fault locations and fault types under different load demands and network conditions show acceptable high accuracy of estimations during fault and fault clearing sequences. Furthermore, the performance of the VSWE is investigated during transients due to switching capacitors, DGs, loads and lines.
机译：对于配电网络中的电能质量（PQ）的担忧，必须使用PQ测量/监视设备来检测和分析PQ问题。由于在所有位置都不能安装这种设备在经济上不可行，因此在最近的研究中一直寻求PQ监视器的最小数量和最佳位置，以同时满足经济效率和监视能力。着重于电压突降，本文尝试在保持所需监视能力的同时，进一步减少通过最佳分配方法获得的PQ监视器的数量。与其他PQ问题相比，越来越多的对电压骤降敏感的电气设备引起了更多与电压骤降相关的经济损失的担忧。在此，提出了一种放置在受监控位置的神经估计器，以使用局部测量值来估计未受监控的敏感负载的瞬时电压骤降波形。回波状态网络（ESN）被用作电压骤降波形估计器（VSWE）。由于分布式发电（DG）的普及及其对电压暂降性能的影响，它们被认为对估算任务提出了挑战。在IEEE 37总线网络上对基于ESN的VSWE进行了研究。在不同的负载需求和网络条件下，针对具有不同故障抗力，故障起始角度，故障位置和故障类型的大量未见案例进行的测试表明，在故障和清除故障序列期间，估算的准确性很高。此外，在开关电容器，DG，负载和线路引起的瞬变期间，研究了VSWE的性能。

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