Using State Estimation for parameter and model error identification

It is pointed out that the uncertainty in an analog measurement is a combination of systematic error and random error; The systematic error completely dominates the random error and varies in a recognizable pattern over the range of measurement. A procedure is presented for reducing the systematic errors and estimating the prevailing standard deviations. A range of analog measurements is obtained from a transmission station, spanning peak and light-load conditions. Then the redundancies in measurements are used to formulate several functions relating these measurements with their attending errors. Minimization of these functions yields the required correction coefficients which are used to minimize the systematic errors and to evaluate the actual random errors, both of which are required by the state estimation. The approach developed uses voltage, real, and reactive power measurements which generally are available and used by state estimators. Current measurement is limited to a critical line or transformer per substation. The effectiveness of the proposed approach is confirmed by an experiment involving a 500/230 kV transmission station.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

This paper presents a review on major blackouts occurred in power grid across the world and importance for the need of state estimation (SE) solution. This work introduces the applications of phasor measurement units (PMU) to reduce the occurrence of blackouts in power system. The blackouts in power system can occur due to overload, light load conditions, heavy storms or due to line outages. The cascade failure due to line outages, i.e., measured as N-1 outages can lead to power system blackout. The necessity of power system state estimation (PSSE) solution for protection in view of blackouts is discussed in this paper. A Generalized Linear State Estimation (GLSE) method is proposed to obtain robust and accurate states of power system network. A matrix integrating phasor and conventional measurements are formulated to obtain accurate states. IEEE transmission bus network such as 14-bus network is considered as test case for proposed GLSE programming in MATLAB. The accurate states are computed and compared with standard weighted least squares (WLS) method to show its effectiveness.

In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs) in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB) are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of single line and no line contingency are considered in observability analysis to ensure protection and control of power system from abnormal conditions. Modeling of ZIB constraints is included to minimize the number of PMU network allocations. This paper presents optimal allocation of PMU at sensitive buses with zero injection modeling, considering cost criteria and redundancy to increase the accuracy of state estimation solution without losing observability of the whole system. Simulations are carried out on IEEE 14, 30 and 57 bus systems and results obtained are compared with traditional and other state estimation methods available in the literature, to demonstrate the effectiveness of the proposed method.

While various industries heavily rely on electrochemical sensors for air and liquid quality monitoring and control, many electrochemical sensors are cross-sensitive to one or more interfering gases in addition to the target component, which may cause incorrect readings and lead to improper control measures. In this study, we proposed a direct algebraic approach-based state estimation and output decomposition algorithm which is suitable for state estimation for a class of nonlinear dynamic systems with output measurements that are highly cross-sensitive to multiple interfering gases. Using the cross-sensitive sensor measurement and its successive time derivatives, the proposed methodology can provide a closed-form solution for the actual system state estimation in a systematic way. The established state estimation algorithm was successfully validated in a numeric example. The proposed state estimation and output decomposition algorithm is applicable to a broad class of nonlinear dynamic systems which use sensors that are cross-sensitive to multiple interfering gases in order to improve the performance of the systems.

Although the normalized Lagrange multiplier (NLM) method has been shown to be very effective for network parameter error identification, errors in parameters corresponding to insensitive NLMs still remain difficult to detect and correct. This paper proposes an enhanced method for detecting and correcting network parameter errors based on multiple measurement scans. The method is developed by first deriving the relationship between parameter errors and the associated Langrage multipliers in state estimation. This is then used to clarify the reason behind the sensitivity issue of NLMs and the improvements made by performing multiple scans. An approach for estimating the necessary number of scans for satisfying various detection requirements is also proposed. Moreover, a local parameter error correction procedure based on multiple scans is presented, with detailed discussion of the local network selection and the number of required measurement scans. Simulation results in a very large utility system in North America illustrate the effectiveness of the analysis and methods proposed in this paper.

Decoupled State-Estimation in Energy Control Centres

This paper investigates identification of parameter and measurement errors in the power network model. A mixed integer quadratic programming (MIQP) model is proposed in this paper to precisely identify multiple parameter and measurement errors. In the proposed MIQP model, erroneous parameters and measurements are involved with penalty factor in the aim of minimizing weighted least-squares (WLS) objective function. The proposed method has the desired property of accurate identification with avoiding over identification in the meantime. Parameter and measurement errors can also be distinguished simultaneously in MIQP model. All these features are verified via simulations that are carried out on IEEE 14-bus system.

This paper is concerned about phasor-based linear state estimation of three-phase power grids which contain non-transposed lines and unsymmetrical branches. It builds upon our previous work where the linear measurement equations were decoupled into three independent sets via the symmetrical component transformation. Such a transformation may however not fully decouple the measurement equations if there are non-transposed transmission lines and unsymmetrical branches in the system. This paper addresses this limitation by deriving additive terms that compensate for the remaining coupling between sequence measurements after the application of the conventional transformation. It will be shown that general unbalanced three-phase power grids can still be transformed into three decoupled subsystems and their state estimation solution can be obtained in parallel by the proposed approach.

The state estimation tools which are currently deployed in power system control rooms are based on a steady state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper investigates the application of Extended Kalman Filtering techniques for estimating dynamic states in the state estimation process. The new formulated “dynamic state estimation” includes true system dynamics reflected in differential equations, not like previously proposed “dynamic state estimation” which only considers the time-variant snapshots based on steady state modeling. This new dynamic state estimation using Extended Kalman Filter has been successfully tested on a multi-machine system. Sensitivity studies with respect to noise levels, sampling rates, model errors, and parameter errors are presented as well to illustrate the robust performance of the developed dynamic state estimation process.

The transition from passive to active distribution networks has led to a growing use of state estimators (SEs), particularly in applications that require high quality data (e.g., demand management and fault detection). To ensure accurate estimations, a bad data detection (BDD) algorithm capable of detecting errors is needed. While these algorithms exist, network operators require understanding the benefits and limitations of these approaches before adopting a solution for their SEs. This paper compares three Chi-square-based BDD algorithms (conventional and two residual covariance matrix-based) to evaluate their performance in terms of speed and effectiveness. Comparisons are carried out using the IEEE 37-bus test feeder with different bad data scenarios. Results highlight that the conventional algorithm is faster, but its filtering capabilities is more limited. On the other hand, they show that the residual covariance matrix-based algorithms require on average 1594% and 2661% more time to achieve a detection that in certain conditions can be 1.56 and 1.67 times better.

Robust state estimation and real-time dense mapping are two core capabilities for autonomous navigation of mobile robots. Global Navigation Satellite System (GNSS) and visual odometry/SLAM are popular methods for state estimation. However, when working between tall buildings or in indoor environments, GNSS fails due to limited sky view or obstruction from buildings. Visual odometry/SLAM are prone to long-term drifting in the absence of reliable loop closure detection. A state estimation method with global-consistent guarantee is desirable for navigation applications. As for realtime mapping, SLAM methods usually get a sparse map that is not good enough for obstacle avoidance and path-planning, and high-quality dense mapping is often computationally too demanding for mobile devices. Realizing the availability of city-scale 3D models, in this work, we improve our previous work on model-based global localization, and propose a modelaided monocular visual-inertial state estimation and dense mapping solution. We first develop a global-consistent state estimator by fusing visual-inertial odometry with the modelbased localization results. Utilizing depth prior from the model, we perform motion stereo with semi-global disparity smoothing. Our dense mapping pipeline is capable of online detection of obstacles that are originally not included in the offline 3D model. Our method runs onboard an embedded computer in real-time. We validate both the state estimation and mapping accuracy in real-world experiments.

The Australian National Electricity Market Management Company (NEMMCO) uses a variety of tools to assess the real-time security of its power system. NEMMCO and the transmission network service providers are particularly concerned with dynamic security. In addition to power system damping monitors used to measure the system oscillatory damping characteristics, NEMMCO has developed a real-time transient security assessment tool. The state estimator solution is sent to a time domain simulation engine, which monitors the transient stability of the power system for a number of credible contingencies. It was important to use commonly used standard power system analysis tools to help build confidence in the tool amongst the various planning bodies. The tool has proven invaluable in alerting and preventing insecure operation whilst the power system is under abnormal operating conditions. Both the approach and the computational architecture are described. In addition, a number of implementation issues, from the telemetered data, through the state estimation and model conversion, to the analysis phases are addressed.

This paper briefly describes Dominion's use of pseudo-measurements for the enhancement of its state estimator solution. Beyond providing a basic definition of pseudo-measurements and their usefulness, this paper highlights the specific integration of pseudo-measurements into Dominion's EMS platform. The paper concludes with a brief caution on the use of pseudo-measurements and highlights possible areas for further integration and treatment of pseudo-measurements.

A robust dynamic line rating monitoring system through state estimation and bad data analysis