基于相对增益和改进粒子群算法的PSS与直流调制协调策略

现有电力系统稳定器(power system stabilizer,PSS)和直流调制多使用本地信号作为控制器反馈输入信号,控制器间的交互作用可能降低甚至破坏系统稳定性。提出了一种基于广域测量信号的PSS与直流调制协调策略,首先通过留数法选择对于振荡模态可观性较强的广域信号作为阻尼控制器备选反馈信号;其次通过相对增益方法选择使PSS和直流调制交互影响最小的备选信号作为最佳反馈信号;而后设计分散控制器,并运用基于混沌和差分进化的混合粒子群优化算法对PSS和直流调制控制器参数进行协调优化。最后,通过EPRI 36节点系统仿真验证了协调策略的正确性和有效性。

多时滞广域测量电力系统稳定分析与协调控制器设计

根据电力系统存在时滞的差异性,将控制器信号分为本地无时滞控制输入信号和广域异地多时滞控制输入信号两部分,建立了多时滞电力系统的模型。运用Lyapunov-Krasovskii泛函理论推导出了含有时滞分量的线性多时滞电力系统稳定判据,将控制问题转换为线性矩阵不等式的可行性问题,并采用模型降阶方法对原系统进行降阶,实现了含有多个时滞的广域输出反馈控制,并计算出了能够使闭环电力系统稳定的最大时滞时间。讨论了系统稳定判据中时滞分量与广域控制器参数之间的关系,以及对区域间振荡的控制效果。最后以两区域四机系统为例,用时域仿真的结果说明了本文所提出的多时滞控制方法的有效性。

强迫功率振荡源的在线定位分析技术

强迫功率振荡源的在线定位对电网安全稳定运行具有至关重要的作用。基于相量测量装置(phasor measurement unit,PMU)的实测数据和同步发电机转子运动方程,给出振荡过程中发电机机械输入功率波动的计算方法。通过对振荡过程中发电机机械输入功率波动、电气输出功率波动、转速(频率)波动的相位关系的理论分析,给出强迫功率振荡的在线监测分析方法。如果原动机不是强迫扰动源,发电机组因响应外界扰动而处于振荡过程中时,机械功率的波动相位滞后于电气功率的波动相位。由于引进PMU实测数据,所提方法可实现各个发电机组独立分析、相互解耦,避免了引入全网模型带来的分析误差和可靠性降低,减少了在线监测分析的复杂程度。实际算例表明,所提方法能有效判断同步发电机是否为强迫功率振荡源,为低频振荡的分析、控制提供可靠参考。

Practical Implementation and Operational Experience of Dynamic Mode Decomposition in Wide-area Monitoring Systems of Italian Power System

This study presents the assumptions and strategies for the practical implementation of the dynamic mode decomposition approach in the wide-area monitoring system of the Italian transmission system operator,Terna.The procedure setup aims to detect poorly damped interarea oscillations of power systems.Dynamic mode decomposition is a data-driven technique that has gained increasing attention in different fields;the proposed implementation can both characterize the oscillatory modes and identify the most influenced areas.This study presents the results of its practical implementation and operational experience in power system monitoring.It focuses on the main characteristics and solutions identified to reliably monitor the interarea electromechanical modes of the interconnected European power system.Moreover,conditions to issue an appropriate alarm in case of critical operating conditions are described.The effectiveness of the proposed approach is validated by its application in three case studies:a critical oscillatory event and a short-circuit event that occurred in the Italian power system in the previous years,and a 15-min time interval of normal grid operation recorded in March 2021.

Wavelet-based data compression for wide-area measurement data of oscillations

This paper proposes a wavelet-based data compression method to compress the recorded data of oscillations in power systems for wide-area measurement systems. Actual recorded oscillations and simulated oscillations are compressed and reconstructed by the waveletbased data compression method to select the best wavelet functions and decomposition scales according to the criterion of the minimum compression distortion composite index, for a balanced consideration of compression performance and reconstruction accuracy. Based on the selections, the relationship between the oscillation frequency and the corresponding optimal wavelet and scale is discussed, and a piecewise linear model of the base-2 logarithm of the frequency and the order of the wavelet is developed, in which different pieces represent different scales. As a result, the wavelet function and decomposition scale can be selected according to the oscillation frequency. Compared with the wavelet-based data compression method with a fixed wavelet scale for disturbance signals and the real-time data compression method based on exception compression and swing door trending for oscillations, the proposed method can provide high compression ratios and low distortion rates.

Unified Residue Method for Design of Compact Wide-area Damping Controller Based on Power System Stabilizer

A wide-area damping controller(WADC)is effective in damping inter-area low-frequency oscillation(LFO),if the time delay in a wide-area control loop can be properly handled.In order to simplify the WADC design and enlarge the delay adaptation range,the classic power system stabilizer(PSS)is adopted,and a new unified residue(UR)method is proposed for compact WADC design.The strategy of control loop selection is also improved by modifying the relative residue index based on a few dominant oscillation modes.The designed PSSbased compact WADC is as simple as classic PSS with no more than two lead-lag phase compensation units.Case studies are carried out on an IEEE 16-machine 68-bus power system.Simulation results demonstrate that the control loop selection before the WADC design is necessary and that the proposed selection strategy can easily pick out the suitable candidate control loops.In addition,it is feasible for the UR method to design WADCs with different time delays in the selected control loops.All the designed WADCs are effective in damping inter-area LFO and robust to time delay variations under operation conditions.Comparisons among five design methods for PSS-based WADC show that the proposed UR method is superior in delay adaptation,the conciseness of WADC structure and computation speed of parameters.

Recent Developments of FNET/GridEye—A Situational Awareness Tool for Smart Grid

Wide-area monitoring systems(WAMS)are becoming increasingly vital for enhancing power grid operators’situational awareness capabilities.As a pilot WAMS that was initially deployed in 2003,the frequency monitoring network FNET/GridEye uses GPS-time-synchronized monitors called frequency disturbance recorders(FDRs)to capture dynamic grid behaviors.Over the past ten years,a large number of publications related to FNET/GridEye have been reported.In this paper,the most recent developments of FNET/GridEye sensors,data centers,and data analytics applications are reviewed.These works demonstrate that FNET/GridEye will become a costeffective situational awareness tool for the future smart grid.

Doubly-fed Deep Learning Method for Bad Data Identification in Linear State Estimation

With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation,which can:(1)identify bad data under both steady states and contingencies;(2)achieve higher accuracy than conventional pre-filtering approaches;(3)reduce iteration burden for linear state estimation;(4)efficiently identify bad data in a parallelizable scheme.The proposed method consists of four key steps:(1)preprocessing filter;(2)online training of short-term deep neural network;(3)offline training of long-term deep neural network;(4)a decision merger.Through delicate design and comprehensive training,the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information.An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method.Multiple test scenarios are applied,which include steady states,three-phase-to-ground faults with(un)successful auto-reclosing,low-frequency oscillation,and low-frequency oscillation with simultaneous threephase-to-ground faults.The proposed method demonstrates satisfactory performance during both the training session and the testing session.