Dynamic State Estimation for Power Systems with Complex-valued Measurements

The widespread deployment of Phasor Measurement Units (PMUs) on power systems has facilitated the real-time monitoring of the power systems dynamics. Dynamic State Estimators (DSEs) are used by the investigators to estimate and identify the state variables and parameters of the nonlinear dynamic models within the power systems by using the measurements which are mainly provided by the PMUs. This dissertation addresses fundamental research on dynamic state estimation of the power systems and presents innovative and robust dynamic state estimation approaches for estimation/identification of the state variables/parameters associated with the nonlinear dynamic models within the power systems.

Guest Editorial: Situational awareness of integrated energy systems

Fault recorders can track the transients in the power system during a fault. However, they cannot provide a wide area picture of the power system operating condition. The provision to track the transients is of great interest for the power system operators. In this paper, a two-stage state estimator (SE), based on both conventional and phasor measurement unit (PMU) measurements, is presented. The estimator is able to track the dynamics of the power system states during a fault. The advantage of the proposed SE is that it does not require full power system observability by the PMU measurements. The two-stage SE was successfully tested on the IEEE 14 and 118 bus systems.

Spatial-temporal adaptive transient stability assessment for power system under missing data

Recently, owing to the high penetration of renewable power generations, random access to dynamical loads and wide application of power-electronic devices, the modern power systems exhibit new features such as high dynamics, low inertia and weak voltage and frequency support. As a powerful tool in revealing the dynamical evolution of the modern power systems, the dynamic state estimation (DSE) scheme gains a surge of research enthusiasms. Note that in actual power systems, various styles of power generations and mixed measurements generated by the supervisory control and data acquisition (SCADA) system and the phasor measurement unit (PMU) pose higher demands on the DSE. In addition, the measurements with anomalies such as gross errors, outliers and incomplete information are often encountered due to the unreliable measurement facilities, constrained communication resources and cyber-threats. Such anomalies, if not tactfully handled, would largely impair the DSE performance. In this paper, a systematic and timely review with respect to DSE for power systems is provided. First, several typical models for power systems include quasi-steady model, synchronous-generator-based multi-machine model and renewable power generation model are introduced. Then the measurement facilities of power systems and their features as well as the measurements with anomalies are reviewed, respectively. Subsequently, the DSE schemes are recalled and discussed from the perspectives of framework, methodology, anomaly-resistant ability and application, respectively. Finally, some possible future research topics are outlined for the DSE of power systems.

Short-term voltage stability (STVS) problem has become more prominent in today’s power systems, and the deployment of phasor measurement units (PMU) opens the way for real-time STVS assessment. The existing approaches for real-time STVS assessment suffer from long observation windows and lack mitigation to the impact of PMU measurement noise. Considering those inadequacies, this paper proposes a noise-tolerant temporal-adaptive STVS assessment approach with the following salient features: 1) the adoption of probabilistic classification enables the early unstable event detection ability, which significantly improves the assessment speed; 2) the probabilistic classifier consists of noisy-tolerant ensemble models that are able to learn the impact of measurement noise, which improves the assessment robustness against measurement noise. The proposed approach is tested on New England 39-bus system, and its fast assessment speed and excellent robustness against measurement noise are verified by comparative studies with existing approaches.

This paper presents a novel coordinated algorithm, Fast Distributed State Estimation with Linear Coordination (FDSELC), considering Phasor Measurement Units (PMUs) measurements at both subsystem and coordination levels. At the subsystem level, the estimators apply augmented Fast Decoupled State Estimation algorithm to reduce the computational time at the first step. At the coordination level, linear estimators based on PMUs measurements are able to coordinate the voltage states of boundary buses, which lead to the further improvement of computational performance. Tests on the IEEE 118-bus system demonstrate the performance of the novel FDSELC algorithms. The results are compared with centralized and other distributed state estimation algorithms in terms of both the estimation quality and computational cost. In addition, the proposed FDSELC algorithm with the distributed control architecture is suitable for future smart grids. In this situation, a large power grid can be decomposed into subsystems, and those subsystems can be estimated, monitored and controlled separately.

The widespread implementation of Phasor Measurement Units (PMUs) on power systems has facilitated the development of accurate and fast dynamic state estimators (DSE), which helps to reliably achieve the real-time monitoring and control of the power systems dynamics. Dynamic state estimators are used by the investigators to estimate and identify the state variables and other parameters associated with the nonlinear dynamic models within the power systems, by using the measurements which are mainly provided by the PMUs. This paper primarily tries to present an innovative and robust dynamic state estimation approach when the PMU data are missing due to various reasons. The highly efficient Unscented Kalman Filter (UKF) algorithm is used to formulate the DSE. The simulation results on IEEE 14 bus system shows that the proposed algorithm remains robust and produce accurate estimates during the missing of PMU data coming from generator buses.

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

Synchrophasor technology contributes significantly to the power system transformation into smart grid since it enables the wide area monitoring and control concept. At the same time, its integration to the smart grid imposes new cyber security challenges in these cyber-physical systems. The key element of the synchrophasor technology is the Phasor Measurement Unit (PMU) which provides synchronized measurements to the control center with a fast reporting rate. The PMU measurements are kept in a Phasor Data Concentrator (PDC) which collects and time aligns measurements from different PMUs. The communication between PMUs and PDC is defined by the synchrophasor communication standard, C37.118.2-2011, which lacks security mechanisms and hence it is vulnerable to cyber-attacks. Consequently, an intrusion detection system (IDS) that can detect PMU measurement manipulation is necessary to maintain the integrity and reliability of the PMU measurements. In this paper, a Synchrophasor Specific Intrusion Detection System that utilizes a PMU's behavioral model to detect data manipulation attacks against PMU measurements is proposed. The proposed IDS approach is tested and validated using a hardware in the loop setup for power systems considering an actual PMU.

Phasor measurement units (PMUs) are being deployed in incremental fashion in power systems. While PMUs may improve the quality of the state estimator (SE), the limited number of PMUs installed may not ensure complete observability of the system. Hybrid state estimator (HSE), considering PMU as well as conventional measurements, is therefore still needed for monitoring the power system. This article proposes a new algorithm belonging to the group of HSEs. Even in case of incomplete observability with PMUs, a two-step state estimation algorithm is presented to optimally utilize the PMU measurements, along with the conventional measurements from supervisory control and data acquisition (SCADA) system. A projection statistic based robust SE is used, when both SCADA and PMU measurements are available. During the interval between two successive arrivals of SCADA measurements, a linear SE is proposed, to exploit the available PMU measurements. The particular advantage of the proposed algorithm is its robustness and insensitivity to bad data. The proposed methods are successfully tested on IEEE-14 bus, 118 bus, and NRPG Indian 246 bus systems.

The subject of the article is the technique for determination of the automatic excitation controller (AEC) parameters based on measurements from phasor measurement units (PMU). This paper shows the possibility of using PMU measurements for determining the synchronous generator AEC parameters. The article considers the type of the AEC which is ARV-MT of the Russian production. By using the simulation in the hardware and software system RTDS (Real-Time Digital Simulator), PMU measurements have been obtained for different modes of the AEC operation. Processing of the results has been carried out in MATLAB. The developed technique is based on the processing of obtained measurements by optimization algorithm. As a result of the comparative analysis, the most suitable type of optimization has been chosen. The possibility of PMU measurements use for determining the AEC parameters in different modes of its operation is shown. Also, the results of application of the technique for cases of a different power level noise in the circuit is investigated. The developed technique determines the AEC parameters with a sufficient accuracy by using PMU measurements. Thus, the technique allows one to extend the field of PMU application, and, being further developed, will allow one to monitor the AEC and calculate its parameters in real time.

Nowadays, the increase in electrical energy consumption and power system restructuring have posed new challenges to the operation, control, and monitoring of power systems. In this situation, the supervisory control and data acquisition (SCADA) system is not enough to ensure power system security and stability. The SCADA is often unable to measure data of all buses simultaneously. In addition, the sampling rate in this system is not enough for some power system applications. Therefore, the information obtained from SCADA does not show power system dynamics properly. In order to improve the power system monitoring, wide area measurement system (WAMS) has been developed to overcome the problems of SCADA system. Phasor measurements units (PMUs) are the main part of WAMS system and it basically consists of three essential processes including collecting, transmitting, and analyzing data. WAMS receives obtained data via a high speed communication links. After data processing and extracting appropriate information, decisions are made to improve the power system performance. Efficient use of power system data to achieve a secure operation strategy is targeted using the WAMS system. Due to the effective role of WAMS and PMUs in the reliable operation of power system, it is necessary to study their concept and applications in this chapter. The history of PMU and its structure is presented in this chapter. In addition, the necessity of WAMS for future power system and its difference from SCADA system have been investigated in this chapter. Different algorithms and application of the WAMS are also introduced in this chapter which can be implemented to improve the performance of the future power system.

Deep learning-based transient stability assessment framework for large-scale modern power system

The widespread deployment of Phasor Measurement Units (PMUs) makes it possible to monitor the power system operating conditions on-line in near real time. Among all operating conditions, forced oscillations (FOs) have came into people's attention and been studied only recently. Mostly, FOs are rogue inputs to the system originating from malfunctioning equipment. The FOs have a negative impact on the estimation of system modes and mode shapes when not properly accounted for. In this paper, the change-point detection decision statistics based upon the output-only covariance subspace method proposed in our previous work [1] is used in a sequential detection method, the cumulative sum (CUSUM) procedure, to achieve better detection performance. We tested the procedure to detect the low-amplitude forced oscillations in power systems buried in system nature responses and find that the algorithm is able to achieve lower false alarm and missed detection rate at the same detection delay as compared with our previous work.