Adaptive Wide-Area Control of Power Systems Through Dynamic Load Modulating SVCs

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.

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 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.

Recently, many aspects of power system dynamic analysis, including oscillation identification, load model identification and system state estimation, are conducted based on the measurement data from phasor measurement unit (PMU). However, the impact of PMU measurement error, especially in power system dynamic process, has an assignable impact on these analysis results and thus cannot be neglected. In this paper, the requirements from IEEE standards and the analyze method for PMU measurement error are proposed first. Then, the ambient signal based identification method and the impact of measurement error for dynamic load model parameters is introduced. Afterwards, the stochastic distribution and frequency spectrum of PMU measurement error are measured through an experiment carried in a power system hardware simulation platform. Finally, the impact of PMU measurement error on the identification results of load model is simulated in WSCC 9 bus system.

A key element in the development of smart power transmission systems over the past decade is the tremendous advancement of the Wide-Area Measurement System (WAMS) technology, also commonly referred to as the Synchrophasor technology. Sophisticated digital recording devices called Phasor Measurement Units or PMUs are currently being installed at different points in the North American grid, especially under the smart grid initiatives of the US Department of Energy, to record and communicate GPS-synchronized, high sampling rate (6-60 samples/sec), dynamic power system data. Significant research efforts have been made on techniques to useWAMS for monitoring and situational awareness of large power networks dispersed across wide geographical areas. In contrast, use of WAMS for automatic feedback control has received less attention from the research community. The objective of this paper is to bridge this gap by formulating wide-area control problems for oscillation damping, voltage control, wide-area protection, and disturbance localization. We present the main research challenges that need to be overcome to realize the benefits of wide area control in power systems. Our discussion begins with a review of the fundamental physical models of different characteristic components of a large transmission-level power grid such as synchronous generators, transmission lines, and loads, followed by a description of how these subsystem-level models can be integrated to form the overall system model. We pose ten distinct control-theoretic problems. The first two problems are on using PMU measurements from selected nodes in the system to identify such system models in different resolutions in real-time, and the remaining on how the identified models can be used for designing output-feedback based damping controllers, for understanding voltage fluctuations at different nodes of the network graph, and for detecting malicious inputs entering the system dynamics via faults or extraneous attacks. We also propose two new control paradigms, namely a scheduling approach for appropriate controller selection based on online estimation of oscillation modes, and distributed phasor-based control using model estimation. We illustrate our ideas via representative examples, many of which are inspired by well-known power transfer paths in the US west coast grid, also referred to as the Western Electricity Coordinating Council (WECC).

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.

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.

This paper proposes an online identification method utilizing phasor measurement unit (PMU) measurements, symmetric dot pattern (SDP) graphs, and the ResNet50‐L1 M model to facilitate the rapid identification of metering relative errors among interconnected capacitor voltage transformers (CVTs) in wide‐area substations within power systems. First, PMUs measured the three‐phase voltage data from interconnected CVTs across wide‐area substations. The amplitude differences of the three‐phase voltages were converted into Euclidean distances, which were then transformed into SDP graphs, with each type of metering relative error corresponding to a distinct SDP graph. The ResNet50‐L1 M model was established by integrating an L1 norm‐based attention mechanism into the ResNet50 architecture. This model was then trained on CVT SDP graphs representing various error states to develop an online error identification system. The effectiveness of the proposed method was evaluated based on data from the “Houshou transmission line” in Ningxia province, which connects the 330 kV Niushoushan substation and the 330 kV Houqiao substation operated by the State Grid Corporation of China. With 16 different error states tested, the model achieved an identification accuracy rate of 89.39%, demonstrating a notable improvement over other methods in comparative tests.

Microgrids suitable operation depends on the system's proper design, based on accurate component models. A complete model considering entire system dynamics is a vital necessity for a successful design. Using dynamic load in microgrid small‐signal model results in a model that shows transient and steady‐state dynamics, since designing a low‐inertia system like microgrid need extra accuracy. In this paper, an inverter‐based microgrid's small‐signal model in islanded operation mode containing a dynamic load model has been achieved. The Exponential Recovery Load (ERL) model is presented here to study the dynamic behavior of load. Microgrid stability analysis has been carried out for both static and dynamic load models. To attain this goal, the entire component equations are obtained and linearized around an operating point, then the state‐space model is formed and eigenvalues are derived. System stability condition has been investigated by plotting eigenvalues and root locus method. In the final step, a simulation on Matlab/Simulink is performed to show the microgrids operation in the presence of a static and dynamic load.

Summary form only given. Following the Northeast blackout of 2003, the Wide-Area Measurement Systems (WAMS) technology using Phasor Measurement Units (PMUs) has tremendously matured for the North American grid. However, with the rapidly increasing number of deployed PMUs, the design and deployment of an efficient wide-area communication and computing infrastructure remains one of the greatest challenges to harvest the gigantic volumes of PMU data in real-time. Neither the architecture of such networks nor the impacts of their operational uncertainties such as delays and data losses on the envisioned control actions are well understood in the current state of art. This tutorial talk will address this gap, and propose a highly resilient, fault-tolerant, and reliable distributed network control system for tomorrow's power grids using cutting-edge emerging technologies such as cloud computing and software defined networks. The proposed architecture will be realized via three interactive layers. Layer 1 will consist of physics-based controllers for power oscillation damping. Layer 2 will contain delay control rules for the communication network that work in tandem with the grid controllers. Layer 3 will consist of a supervisory controller realized through embedding and reconfiguration rules in a distributed cloud environment that continuously monitors the system status, and ensures fault-tolerance, resilience, and reliability of the overall closed-loop control system.

Mitigating the impact of false data in wide area control of power systems

Accurate real-time states provided by the state estimator are critical for power system reliable operation and control. This paper proposes a novel phasor measurement unit (PMU)-based robust state estimation method (PRSEM) to real-time monitor a power system under different operation conditions. To be specific, an adaptive weight assignment function to dynamically adjust the measurement weight based on the distance of big unwanted disturbances from the PMU measurements is proposed to increase algorithm robustness. Furthermore, a statistical test-based interpolation matrix ${H}$ updating judgment strategy is proposed. The processed and resynced PMU information are used as priori information and incorporated to the modified weighted least square estimation to address the measurements imperfect synchronization between supervisory control and data acquisition and PMU measurements. Finally, the innovation analysis-based bad data (BD) detection method, which can handle the smearing effect and critical measurement errors, is presented. We evaluate PRSEM by using IEEE benchmark test systems and a realistic utility system. The numerical results indicate that, in short computation time, PRSEM can effectively track the system real-time states with good robustness and can address several kinds of BD.

The dynamic effects of Static Var Compensator (SVC) and Thyristor-controlled Series Capacitor (TCSC) controllers on preventing voltage collapse are analyzed comprehensively by the small signal method. Hopf bifurcations caused by the interaction between SVC and dynamic load are investigated. The effects of the time constant selections for SVC and TCSC on voltage stability are also discussed. The authors show that TCSC is more effective than SVC in preventing voltage collapse. The steady-state stability limit of system is increased when SVC or TCSC is installed.

As an extension of previous studies on modeling and identification of nonlinear dynamic load models, this letter describes the influence of the normalization in dynamic reactive load models. Using measured data from normal operation, it is shown that the reactive power level, which has previously used as normalization factor is inappropriate. If instead apparent power level is used, the variability in the parameters that describe the reactive load response is drastically reduced.

Not only static load model but dynamic load model is necessary for improving the accuracy of time-domain simulation results because only dynamic load model can express the dynamic response of the load. The measurement based auto-derivation method for estimating load model parameters from PQVF monitoring devices has been developed. The PQVF monitoring device which has already been installed into power system captures disturbance data triggered by voltage deviation and frequency deviation. The transfer-function based load model which consists of voltage characteristics index, frequency characteristics coefficient, transient gain, and time constant was used as the dynamic load model. The simple transfer function-based dynamic load model can express 80% of 182 load responses precisely. Therefore, the transfer function-based dynamic model can have applicability to represent the dynamic behavior of the load response concisely and accurately on condition that the quantity of voltage drop during the disturbance is less than 30%.