Energy Function Analysis of Power Transfer Paths Using Synchronized Phasor Data

Between 2005 and 2015, US electricity sector emissions of nitrogen oxides and sulfur dioxide, which harm human health and the environment, declined by two thirds, and many coal‐fired power plants became unprofitable and retired. Intense public controversy has focused on these changes, but the literature has not identified their underlying causes. Using a new electricity sector model of the US eastern interconnection that accurately reproduces unit operation, emissions, and retirement, we find that electricity consumption and natural gas prices account for nearly all the coal plant profitability declines and resulting retirements. Environmental regulations had little effect on these outcomes.

The US Eastern Interconnection is dividing into Regional Transmission Organizations. This paper discusses the present state of reorganization and then near and far term perceptions on how grid expansion is to be accomplished. Who will be the stakeholders, the regulators, the customers, the owners? Who will pay for the expansions? How will reliability be assured?.

Voltage stability is a security concern for modern power systems. It can be analyzed using detailed or equivalent models. In this paper a new approach is presented for voltage stability analysis using synchronized phasor measurement data. Simple equivalent models of the interconnected system and load side at a measurement point are estimated from the data, and then used for calculating PV curves and predicting the stability limit. Two different models are proposed, and compared based on the analyses performed on the event recordings from US western power system. Minimal modeling and formulation makes the method suitable for online calculations. The models are continuously updated to reflect the effects of different system components and changes

In this paper we develop a measurement-based method for estimating a two-machine reduced model to represent the interarea dynamics of a radial, two-area power system with intermediate voltage support. The method uses synchronized bus voltage phasor measurements at three buses including the voltage control bus, and the line currents on the power transfer path.

The accurate real-time state estimation is needed to perform real-time monitoring and control. However, the current power system uses SCADA technology as a tool for monitoring and control. SCADA existing measurement device has a few disadvantages: low accuracy, low sampling rate in measuring the measurements data, and asynchronous measurements between instrument. Due to those disadvantages, state estimation resulted has low accuracy, and not in real-time. These disadvantages can be minimized by installing several phasor measurement unit (PMU). IEEE-30 bus test system is applied to simulate the proposed technique. The result shows, the required PMU should be installed in power system that already has 116 conventional SCADA measurements is 6. The location of these PMUs are in buses 2, 10, 12, 15, 25, and 27. That result is achieved in 320 iteration of genetic algorithm (GA). In this paper, mean absolute error (MAE) is used to asses the state estimation accuracy. The value of $\overline {MAE} $ system with PMU is 7.20718X10−6. While, the value of $\overline {MAE} $ system without PMU is 0.110162054.

State estimation is the core operation performed within the energy management system (EMS) of smart grid. Hence, the reliability and integrity of a smart grid relies heavily on the performance of sensor measurement dependent state estimation process. The increasing penetration of cyber control into the smart grid operations has raised severe concern for executing a secured state estimation process. The limitation with regard to monitoring large number of sensors allows an intruder to manipulate sensor information, as one of the soft targets for disrupting power system operations. Phasor measurement unit (PMU) can be adopted as an alternative to immunize the state estimation from corrupted conventional sensor measurements. However, the high installation cost of PMUs restricts its installation throughout the network. In this paper a graphical approach is proposed to identify minimum PMU placement locations, so as to detect any intrusion of malicious activity within the smart grid. The high speed synchronized PMU information ensures processing of secured set of sensor measurements to the control center. The results of PMU information based linear state estimation is compared with the conventional non-linear state estimation to detect any attack within the system. The effectiveness of the proposed scheme has been validated on IEEE 14 bus test system.

This paper describes the implementation, testing, and performance of phasor measurements in an industrial state estimation at the New York Power Authority (NYPA). Phasor Measurement Units (PMU) with GPS synchronization were incorporated into the data acquisition subsystem of the Energy Management System (EMS). The modified State Estimator was subjected to pre- and post-field installation testing. The pre-field installation testing was done to verify the correctness of the solution algorithm, to identify the impact of phasor metering accuracy on the quality of estimator solution, to show the relative effectiveness of phasor measurements with respect to other measurements, to identify the effect of the reference bus selection, to detect the impact of PMUs on the State Estimator convergence, and to establish the minimum clock synchronization sampling accuracy required for the PMUs [10]. The post-field installation tests were conducted to identify the effectiveness of the PMUs in the State Estimator solution using the real-time data as well as to detect the effect of time skewing and measurement weights on the State Estimator solution.

Hybrid measurement of the phasor measurement units (PMU) and the supervisory control and data acquisition (SCADA) system is used for state estimation of power transmission system when PMU deployment can not cover all buses. The traditional iterative solution process of state estimation is low efficient and may cause truncation error. A fast state estimation method based on pseudo-measurement modeling of extreme learning machine (ELM) for power transmission system state estimation is proposed. The injected powers measured by SCADA are used as input, the real and imaginary parts of bus voltages are used as output. The pseudo-measurement value model and pseudo-measurement error model are trained using historical data. In the new time, the pseudo-measurements obtained from the trained model are combined with the PMU measurements for fast linear state estimation. The final state estimation results can be obtained quickly. Simulation results of IEEE 14 system show that the proposed method not only improves the accuracy of state estimation, but also greatly reduces the calculation time. It can provide basic information for other applications in EMS accurately and quickly.

Accurate state estimation is essential for correct supervision of power grids. With the existence of cyber-attacks, state estimation may become inaccurate, which can eventually lead to wrong supervisory decision making. To detect cyber-attacks in power grids equipped with phasor measurement units (PMUs), a new intrusion detection system based on clustering approach, called PMU Intrusion Detection System (PMUIDS), is proposed in this article. After solving the optimal PMU placement in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -1 contingency, several static state estimations are obtained by removing the measurements of one PMU in each time. The resulting state vectors are clustered in two steps: First, subtractive clustering is employed to obtain the number of clusters, which determines the number of integrity attacks; and second, fuzzy C-means clustering assigns the state vectors to the corresponding clusters, which determines the attacked PMUs. In addition, two theorems are proved, which indicate that the attacker cannot coordinate successful stealth attacks in cases that by removing attacked PMUs from state estimation, the power system still remains full observable. Furthermore, in the case of possible stealth attacks, the attacker cannot falsify the estimation of any arbitrary state variable. The hardware-in-the-loop results on a sample power system show that the proposed approach can detect integrity attacks, determine the number of attacks, obtain the correct state vector, and localize the attacks, even in case of multiple simultaneous attacks.

State estimation is used currently in wide area electrical systems for real time analysis. Studies have shown that in HV transmission networks, where system-wide synchronized phasor measurement units are installed, voltage angle measurements can be included in the input measurements data set, with the result of improving the estimation precision. The authors developed in previous papers a SE algorithm based on Multilayer Perceptron Artificial Neural Networks. This paper extends this research by using PMU voltage magnitude and angle measurements in the input data for the ANN estimator, and shows in a case study that the estimation precision is improved. The development of advanced electricity markets in Europe and North America changed the operation and dispatch rules of power systems. In the United States and Canada, the ISO/RTO model is used to manage grid operation for approximately a half of North American Continent (1), while in Europe continuous efforts are made for coupling the national and regional electricity markets into a common market, markets supplying 75% of the demand using now a common day-ahead price mechanism and cross-border capacity management (2). High-performance tools are required for real time monitoring and control of such large systems. Enhanced state estimation (SE) algorithms are robust tools used today by system operators. Phasor measurement units (PMU) are used for synchronizing measurements used in state estimation analyses for systems covering wide geographical territories (3), and the trend is to generalize their deployment also at distribution level (4,5). Other approaches use distributed estimation, by splitting one large system in several areas estimated locally (6). The two approaches can be also combined (7). Recent research also use artificial neural networks (ANN) for state estimation (8,9). The authors proposed in (10) a SE approach which uses Multilayer Perceptron (MLP) artificial neural networks. A case study performed on a section of the Romanian HV distribution system was carried out, and the estimation results have shown a good precision. This paper extends the study by including PMU measurements into the measurement input set to improve the previous estimation results. The expected results are the improvement of estimation, a behaviour similar to classic algorithms.

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.

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.

Synchronized Phasor Measurement Units (PMUs) are becoming a key element of monitoring, protection, control and state estimation applications in power systems and are the basis for the implementation of Wide Area Monitoring Systems (WAMSs). This paper investigates the possibility of using a software-only PTP (Precision Time Protocol) synchronization method to synchronize different Phasor Measurement Units (PMUs) and how this synchronization impacts the State Estimation. In this context, as a case study, the output data of the PMUs have been used to estimate the state of a power system of reduced size and complexity. A laboratory experimental setup has been built to evaluate the performance achievable with the proposed synchronization solution. The results thus obtained and the performance analysis are discussed in this paper.

This paper presents a privacy preserving approach to decentralized state estimation in multi-area power systems. By formulating state estimation as a model-distributed regularized least-squares (MDRLS) problem, we ensure that the state variables and system matrix of each area are hidden from all other areas in order to protect privacy and sensitive information. We present a scheme that solves the primal MDRLS problem using the alternating direction method of multipliers, and a second method that solves the dual problem using a distributed form of the coordinate descent algorithm. Only information related to current measurements on tie-lines linking neighboring areas is exchanged between those areas. The proposed schemes enable the local state estimator in each area to estimate the voltage magnitude and phase angle of each bus in its own control area from phasor measurement units (PMU) without the need for full local PMU-observability. The novelty of the proposed methods is in that they employ the inherently hierarchical architecture of the wide-area monitoring system to perform decentralized state estimation. Our simulation results show that the estimation error of both methods converges to that of the centralized approach.

This paper describes the capabilities, calculation logic, and foundational assumptions of EPfast, a new simulation and impact analysis tool developed by Argonne National Laboratory. The purpose of the model is to explore the tendency of power systems to spiral into uncontrolled islanding triggered by either man-made or natural disturbances. The model generates a report that quantifies the megawatt reductions in all affected substations, as well as the number, size, and spatial location of the formed island grids. The model is linear and is intended to simulate the impacts of high-consequence events on large-scale power systems. The paper describes a recent application of the model to examine the effects of a high-intensity New Madrid seismic event on the U.S. Eastern Interconnection (USEI). The model's final upgrade and subsequent application to the USEI were made possible via funding from U.S. Department of Energy's Office of Infrastructure Security and Energy Restoration.