Traditional State Estimation Research Articles

RETRACTED: A Robust WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology

With the development of modern society, the scale of the power system is rapidly increased accordingly, and the framework and mode of running of power systems are trending towards more complexity. It is nowadays much more important for the dispatchers to know exactly the state parameters of the power network through state estimation. This paper proposes a robust power system WLS state estimation method integrating a wide-area measurement system (WAMS) and SCADA technology, incorporating phasor measurements and the results of the traditional state estimator in a post-processing estimator, which greatly reduces the scale of the non-linear estimation problem as well as the number of iterations and the processing time per iteration. This paper firstly analyzes the wide-area state estimation model in detail, then according to the issue that least squares does not account for bad data and outliers, the paper proposes a robust weighted least squares (WLS) method that combines a robust estimation principle with least squares by equivalent weight. The performance assessment is discussed through setting up mathematical models of the distribution network. The effectiveness of the proposed method was proved to be accurate and reliable by simulations and experiments.

Parallel Domain Decomposition Based Distributed State Estimation for Large-scale Power Systems

Growing system sizes and complexity, along with the large amount of data provided by phasor measurement units (PMUs), are the drivers to accurate state estimation algorithms for online monitoring and operation of power systems. In this paper, a distributed weighted-least-square state estimation method using an additive Schwarz domain decomposition technique is proposed to reduce the computational execution time. The proposed approach divides a data set into several subsets to be processed in parallel using a multiprocessor architecture considering data exchange among distributed areas. The slow coherency method and balanced partitioning are utilized to reduce the communication overhead and increase accuracy. Moreover, bad data analysis is also investigated in a distributed manner. The performance of the proposed distributed state estimator, along with the speed-up for several test systems, was compared with the traditional centralized state estimator. The simulation results show a speed-up of 6.5 for a 4992-bus system.

Event triggered state estimation techniques for power systems with integrated variable energy resources

For many decades, state estimation (SE) has been a critical technology for energy management systems utilized by power system operators. Over time, it has become a mature technology that provides an accurate representation of system state under fairly stable and well understood system operation. The integration of variable energy resources (VERs) such as wind and solar generation, however, introduces new fast frequency dynamics and uncertainties into the system. Furthermore, such renewable energy is often integrated into the distribution system thus requiring real-time monitoring all the way to the periphery of the power grid topology and not just the (central) transmission system. The conventional solution is two fold: solve the SE problem (1) at a faster rate in accordance with the newly added VER dynamics and (2) for the entire power grid topology including the transmission and distribution systems. Such an approach results in exponentially growing problem sets which need to be solver at faster rates. This work seeks to address these two simultaneous requirements and builds upon two recent SE methods which incorporate event-triggering such that the state estimator is only called in the case of considerable novelty in the evolution of the system state. The first method incorporates only event-triggering while the second adds the concept of tracking. Both SE methods are demonstrated on the standard IEEE 14-bus system and the results are observed for a specific bus for two difference scenarios: (1) a spike in the wind power injection and (2) ramp events with higher variability. Relative to traditional state estimation, the numerical case studies showed that the proposed methods can result in computational time reductions of 90%. These results were supported by a theoretical discussion of the computational complexity of three SE techniques. The work concludes that the proposed SE techniques demonstrate practical improvements to the computational complexity of classical state estimation. In such a way, state estimation can continue to support the necessary control actions to mitigate the imbalances resulting from the uncertainties in renewables.

A Method of Introducing PMU Current Measurement to Nonlinear State Estimation

Aiming at the problem that the current measurements provided by phasor measurement unit (PMU) is difficult to introduce to traditional state estimation, a new method to solve the compatibility problems of PMU and supervisory control and data acquisition (SCADA) is presented in this paper. By the rotated-pseudo-measurement (RPM) method, current measurements are transformed into the pseudo measurements and decoupled in polar coordinates, so that they could taken into nonlinear state estimation. Then the RPM method is simulated compared with the current transformation (CT) method. According to the result, it is showed that the RPM method has many advantages compared with the CT, such as independence of other measurements, small error in propagation and more significant in improving estimation accuracy. Moreover, the RPM method has the compatibility with traditional nonlinear state estimation and the fast calculating speed, which have good prospects of practice.

An efficient automated topology processor for state estimation of power transmission networks

A robust network topology processor that can be utilized in both traditional and PMU-based state estimators is developed. Previous works in the field of topology processing are scrutinized and their drawbacks are identified. Building on top of the state of the art, an algorithm covering the limitations of available topology processing approaches and including new features is proposed. The presented algorithm was implemented in MATLAB and tested using two different power networks with detailed substation configurations (bus/breaker models) including a modified version of the IEEE Reliability Test System 1996. As the topology processor is intended to supply network topologies to a PMU-based Sate Estimator, the IEEE Reliability Test System 1996 is simulated in real-time using the eMegaSim Opal-RT real-time simulator which is part of “SmarTS Lab” at KTH Royal Institute of Technology. Testing is carried out through several test scenarios and computation times are calculated. It is shown that the computation times are adequate for supporting a PMU-only state estimator.

Distributed agent-based state estimation considering controlled coordination layer

The buses involved in re-estimation is fixed in the traditional distributed state estimation. On the one hand, if only the boundary buses were re-estimated, will lead to a high power mismatch in a short or low impedance transmission line. On the other hand, if all the buses were re-estimated again, will lead to unnecessary computational burden. To solve this problem, this paper presents a new distributed agent-based state estimation that considers controlled coordination layer. Each local agent of a subsystem is in charge of the state estimation of its own system. The global sensitivity agent is responsible for the sensitivity analysis of the entire system. The boundary bus aggregation agent determines the range of coordination layer to be re-estimated according to the state estimation deviation of each boundary bus defined in this paper. Furthermore, the observability analysis judgment theorem of the overlapped DSE and the topology analysis suitable for the bad data of boundary region are investigated. The proposed method is illustrated with the IEEE 14-bus system, and the test results for the IEEE 118-bus system are provided.

An Efficient State Estimation Algorithm Considering Zero Injection Constraints

Traditional state estimation methods employ large weights for zero injection pseudo-measurements, which may result in numerical instability. The normal equations with constraints will increase the order of the coefficient matrix, thus reducing the speed of the calculations. To improve computational performance, this paper presents a modified Newton method to deal with zero injection constraints. The decoupled form of the proposed technique-a modified fast decoupled state estimation method-is also given. The computational speed of the proposed modified Newton method is similar to that of conventional state estimations based on normal equations, since they use the same type of coefficient matrices. Furthermore, it offers the advantage that zero injection constraints can be strictly satisfied, and the numerical stability problem caused by large weights will no longer exist. Extensive numerical results from test systems and a real provincial system are included to verify the performance of the proposed procedure.

Novel application of detrended fluctuation analysis for state estimation using synchrophasor measurements

The phasor measurement unit (PMU), with its ability to directly calculate synchronized positive sequence voltages and currents (magnitude and phase), offers a more current and accurate view of the power system compared to that provided by traditional measurements and state estimation (SE). However, the high-resolution data, provided at rates of 50 Hz and 60 Hz (dependant on nominal frequency), capture any transient or dynamic events occurring on the network, potentially distorting the intended steady-state view at various points of the power system. In this paper we propose the use of detrended fluctuation analysis (DFA) to detect and isolate such events for alternative investigation. Comparisons are made between the state estimator and installed PMUs on the high-voltage transmission system of Great Britain.

Dead Reckoning in a Dynamic Quadruped Robot Based on Multimodal Proprioceptive Sensory Information

It is an important ability for any mobile robot to be able to estimate its posture and to gauge the distance it traveled. In this paper, we have addressed this problem in a dynamic quadruped robot by combining traditional state estimation methods with machine learning. We have designed and implemented a navigation algorithm for full body state (position, velocity, and attitude) estimation that uses no external reference but relies on multimodal proprioceptive sensory information only. The extended Kalman filter (EKF) was used to provide error estimation and data fusion from two independent sources of information: 1) strapdown mechanization algorithm processing raw inertial data and 2) legged odometry. We have devised a novel legged odometer that combines information from a multimodal combination of sensors (joint and pressure). We have shown our method to work for a dynamic turning gait, and we have also successfully demonstrated how it generalizes to different velocities and terrains. Furthermore, our solution proved to be immune to substantial slippage of the robot's feet.

Harmonic State Estimation on a Radial Distribution System with Distributed Generation

In the present work, a state estimation technique is applied to calculate the amplitudes and phase angles of the harmonic currents injected at the buses of a power distribution system. To this effect, the model of a 50-Hz 13.2-kV 4-bus radial system is used. The grid (source) and a generator are connected at opposite ends of the system, following a configuration known as distributed generation, which is becoming quite usual nowadays. In this particular implementation, the nodal harmonic voltages are presented as the state variables instead of the harmonic currents. This follows the same structure as a traditional fundamental frequency state estimation. As a further step, the injected harmonic currents are calculated from the corresponding injected powers and nodal voltages.

Performance bounds of extended target tracking in cluttered environments

In this paper, we consider the problem of the performance bound of a nonlinear filtering problem corresponding to tracking an extended target in cluttered environments (i.e., with false alarms and missed detections). The high resolution sensor obtains the measurements of the position and the extent of the extended target whose shape is modeled by an ellipse. The posterior Cramer-Rao lower bound (PCRLB) provides a useful tool to evaluate the best achievable performance of the nonlinear filtering problem. The bounds of the traditional kinematic state estimation are calculated using the point and the extended target model. It is shown in this paper that the bound of extended target tracking is smaller than that of point model because more information is utilized. The bounds are calculated to examine the influence of the measuring accuracy, the geometry between the sensor and the target, the prior knowledge of the target, and the environmental circumstance. In a cluttered environment, the PCRLB is calculated by IRF (information reduction factor), MSC (measurement sequence conditioning), and MESC (measurement existence sequence conditioning) approaches. The simulation results also illustrate the relationship of the three methods.

A state estimator including conventional and synchronized phasor measurements

This paper presents an effective weighted least square formulation for the solution of the state estimation problem, considering conventional as well as synchronized phasor measurements. The proposed algorithm is based on a reference-free formulation, using both rectangular and polar coordinates for branch current phasor measurements, and alleviates any numerical problems encountered during initialization stage of the state estimation algorithm. Analytical equations are provided for the conventional and the phasor measurements and their corresponding partial derivatives with respect to state variables. The proposed algorithm is tested with a seven bus system and is compared with the traditional state estimator. Simulation results show that the proposed algorithm improves the precision greatly and gets better behavior as compared with the traditional state estimator.

A Belief Propagation Based Power Distribution System State Estimator

The most popular method used in traditional power system state estimation is the Maximum Likelihood Estimation (MLE). It assumes the state of the system is a set of deterministic variables and determines the most likely state via error included interval measurements. In the distribution system, the measurements are often too sparse to fulfill the system observability. Instead of introducing pseudomeasurements, we propose a Belief Propagation (BP) based distribution system state estimator. This new approach assumes that the system state is a set of stochastic variables. With a set of prior distributions, it calculates the posterior distributions of the state variables via real-time sparse measurements from both traditional measurements and the high resolution smart metering data.

Transition to a Two-Level Linear State Estimator—Part I: Architecture

The availability of synchro-phasor data has raised the possibility of a linear state estimator if the inputs are only complex currents and voltages and if there are enough such measurements to meet observability and redundancy requirements. Moreover, the new digital substations can perform some of the computation at the substation itself resulting in a more accurate two-level state estimator. The main contribution in this paper is that this two-level processing removes the bad data and topology errors, which are major problems today, at the substation level. In Part I of the paper, we describe the layered architecture of databases, communications, and the application programs that are required to support this two-level linear state estimator. In Part II, we describe the mathematical algorithms that are different from those in the existing literature. As the availability of phasor measurements at substations will increase gradually, this paper describes how the state estimator can be enhanced to handle both the traditional state estimator and the proposed linear state estimator simultaneously. This provides a way to immediately utilize the benefits in those parts of the system where such phasor measurements become available and provides a pathway to transition to the "smart" grid of the future.

Transition to a Two-Level Linear State Estimator—Part II: Algorithm

The availability of synchro-phasor data has raised the possibility of a linear state estimator if the inputs are only complex currents and voltages and if there are enough such measurements to meet observability and redundancy requirements. Moreover, the new digital substations can perform some of the computation at the substation itself resulting in a more accurate two-level state estimator. The main contribution in this paper is that this two-level processing removes the bad data and topology errors, which are major problems today, at the substation level. In Part I of the paper, we describe the layered architecture of databases, communications, and the application programs that are required to support this two-level linear state estimator. In Part II, we describe the mathematical algorithms that are different from those in the existing literature. As the availability of phasor measurements at substations will increase gradually, this paper describes how the state estimator can be enhanced to handle both the traditional state estimator and the proposed linear state estimator simultaneously. This provides a way to immediately utilize the benefits in those parts of the system where such phasor measurements become available and provides a pathway to transition to the "smart" grid of the future.

Multiphase transmission line modeling for voltage sag estimation

This paper presents a novel approach for voltage sag indices calculation based on instantaneous voltage estimation. The estimation uses traditional state estimation where redundant measurements are available. The estimation is based on time domain state estimation which uses time domain modeling of the power network. The time domain current monitoring is used to have linear mapping and to achieve high performance of voltage sag estimation. The fault estimation procedure is prior of the voltage sag estimation. This paper shows a possible for fault instance detection, fault location identification and fault type estimation method that are required to estimate voltage sag for different line models utilizing residual analysis and topology error processing. Lumped parameter and distributed parameter transmission line modeling are developed to estimate instantaneous voltage at a three-phase power system in time domain. Magnitude and duration of voltage sag as main indices are calculated from the estimated instantaneous bus voltage. The performance of the novel approach is tested on IEEE 14 bus system and the results are shown.

Fault detection and identification for voltage sag state estimation in power systems

This paper presents a novel approach for fault detection and identification in order to estimate voltage sag during a fault. One of the best approaches for voltage sag estimation is instantaneous voltage estimation. The approach uses traditional state estimation where redundant measurements are available. The proposed method is used to estimate voltage sag performance during a fault. At the fault duration, the grid topology is changed. In such a case the measurement matrix must be re-determined at the fault duration. The proposed approach is an efficient method for fault detection and identification which is based on residual analysis and topology error detection. The performance of the novel approach is tested on an IEEE 14 bus system and the results are shown.

A Method for Voltage Sag State Estimation in Power Systems

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a new technique for voltage sag state estimation. The proposed method is based on estimating the number of voltage sags occurring at nonmonitored buses from the number of sags recorded at a limited number of monitored buses. This problem, contrary to the traditional state estimation where redundant measurements are available, is formulated as an underdetermined system of equations. In this paper, linear integer programming techniques are employed to solve this estimation problem. The performance of the proposed method is tested by means of several case studies applied in the IEEE 24-bus Reliability Test System (RTS) and in the IEEE 118-bus Test System. </para>

An Alternative for Including Phasor Measurements in State Estimators

With the increasing use of real-time synchronized phasor measurement units, it is necessary to consider applications of these measurements in greater detail. One of the most natural applications of these measurements is in the area of state estimation. A straightforward application of state estimation theory treats phasor measurements of currents and voltages as additional measurements to be appended to traditional measurements now being used in most energy management system (EMS) state estimators. The resulting state estimator is once again nonlinear and requires significant modifications to existing EMS software. This paper proposes an alternative approach, which leaves the traditional state estimation software in place, and discusses a novel method of incorporating the phasor measurements and the results of the traditional state estimator in a postprocessing linear estimator. This paper presents the underlying theory and provides verification through simulations of the two alternative strategies. It is shown that the new technique provides the same results as the nonlinear state estimator and does not require modification of the existing EMS software

Global state estimation for whole transmission and distribution networks

Traditionally, transmission and distribution state estimators are studied and developed separately. But lots of large cities have their hybrid transmission and distribution networks. In order to face keen competition in market, the requirement of coordinated operation between transmission and distribution systems is now here. Therefore, in order to balance the boundary mismatch between the transmission and distribution network models existing in the results of traditional state estimation methods, the transmission and distribution networks are studied as a whole in this paper. The weighted least squares (WLS) model for global state estimation (GSE) is presented for estimating the global consistent state in the whole network. A novel master–slave-splitting (MSS) iterative method is developed for solving the hybrid GSE problem. In the proposed method, the GSE problem of large scale is split into a transmission state estimation and lots of distribution state estimation sub-problems, which supports on-line geographically distributed computation. In order to fit the different features between transmission and distribution networks, each sub-problem can be solved with different algorithm. Several case studies are carried out, and the accuracy, convergence, efficiency and reliability of the proposed method are validated.