Dynamic State Estimation Research Articles

Dynamic state estimation of power system using hybrid whale-tunicate optimized unscented Kalman filter based on wide area measurement systems

The Energy Management Systems (EMS) utilizes Dynamic State Estimation to monitor the operating condition of the power grid. For a synchronous generator, the speed and angle of a rotor are the most important dynamic states. These states are responsible for producing uncertainty in the power system models. Extended Kalman Filter (EKF) and Unscented Kalman Filters (UKF) are utilized to estimate the dynamic states of the power system when it perceives significant disturbances or noise. The conventional Unscented Kalman Filter (UKF) has limitations in terms of slow convergence speed and its lack of positive semi-definiteness. This prohibits its ability to accurately track the transients of the system. The scaling parameters of UKF (alpha and beta) are optimally tuned to overcome these issues. In this paper, a novel Hybrid Whale-Tunicate-based UKF (HW-TO-UKF) is proposed for tuning the scaling parameters of UKF for reducing the mean difference between the real state and the measured state. During the validation of the proposed method, better robustness and statistical efficiency are achieved by defining the objective function as the minimization of the error difference between the actual and measured states. The simulation is carried out on the WSCC-3 and NPCC-48 machine systems to demonstrate the performance of the proposed HW-TO-UKF over the existing models.

Enhanced dynamic state estimation of regional new energy power system under different abnormal scenarios

AbstractThe high proportion of renewable energy sources in the power grid increases the failure probability of the system, which becomes a new challenge for the safe and stable operation of the regional power grid. To ensure stable control of the power network with substantial renewable energy integration, this article proposes a new method that combines the long short‐term memory (LSTM) neural networks and adaptive cubature Kalman filter (ACKF) to improve the prediction accuracy of mutation data inherited from the renewable generation. Four abnormal scenarios, including low voltage ride‐through (LVRT), high voltage ride‐through (HVRT), continuous fault ride‐through and bad data injection of the regional power grid are investigated through extensive case studies. The proposed method is implemented on the IEEE 30‐node system for performance verification. The simulation results demonstrate that the proposed method has considerably higher robustness than the traditional Kalman filter algorithm and can effectively improve the overall state estimation accuracy of the renewable energy power system under different scenarios.

Dynamic state estimation of distribution networks with mixed measurements based on Cubature Kalman Filter

The integration of synchronized phasor measurement units (μPMU) has introduced diverse data sources into the distribution network. Each has distinct time scales and precision levels. To enable effective decision-making, it is crucial to amalgamate these measurements efficiently, ensuring both speed and accuracy in distribution network state estimation. This study explores a dynamic state estimation approach for distribution networks, employing Cubature Kalman Filtering (CKF) as the central methodology. By deeply fusing μPMU, AMI and SCADA data and incorporating CKF techniques, it improves accuracy and stability, which is validated on the IEEE 37-node system. This research supports advanced distribution network apps and large-scale data analysis.

A dynamic state estimation method for integrated energy system based on radial basis kernel function

For state estimation (SE) of dynamic electro-thermal gas coupled systems, measurements usually assume that the measurement noise obeys a Gaussian distribution. The extended Kalman filter and the unscented Kalman filter (UKF) are some of the widely used estimation methods in SE. However, the measurement noise does not always follow Gaussian distribution in practice. When the measurement noise is non-Gaussian, the performance of these methods may not be satisfactory. In this paper, we propose an unscented Kalman filtering method based on minimizing radial basis kernel function criterion (MRBFC-UKF), which explores the optimal values of the shape parameters of the kernel function instead of using the widely used Gaussian and exponential kernel functions. Simulations are run dynamically in an integrated energy system which comprises an IEEE 14-bus, a 20-node natural gas network and a 32-node local thermal network. The results show that the proposed MRBFC-UKF method has good robustness and accuracy, and can effectively cope with the presence of unexpected bad data inputs.

Using dynamic state estimation to detect loss of excitation in synchronous generators

AbstractOne of the common methods to detect Loss of Excitation (LOE) is the impedance‐based technique. But it is too slow, and may show incorrect performance when faced with power system disturbances like stable power swings (SPS). To improve the aforementioned problems, a new index is introduced based on the change of transient states of the synchronous generator which are estimated by Extended Kalman filter (EKF). The principal idea of the proposed method is that the angle between the transient voltage and q‐axis internal voltage phasor will be increasing during LOE events. Therefore, this angle and its derivative are used. Estimation of the internal variables of the generator is used to achieve the angle between transient voltage and internal voltage phasor. To evaluate the efficiency of this algorithm, numerous simulations are performed on various networks and in different operating conditions. The simulation results of the method demonstrate that this approach both reduces the detection time in most cases and improves its security in the encounter of the other disturbances. Experimentally, the performance of the proposed method is evaluated by using a 5 kVA laboratory generator. Experimental studies show that the presented algorithm performed correctly detects LOE and SPS conditions.

Distributed Dynamic State Estimation of Power Systems Considering Dynamic State Constraints based on Phasor Measurements Units

Background: Dynamic state estimation can provide detailed information about power systems. However, there is no clear, dynamic equation for the state transition of bus voltages in power systems. Currently, smoothing methods based on historical data are commonly used, but they cannot ensure accurate state prediction in power systems with a large amount of renewable energy. Moreover, the fast sampling rate of phasor measurement units generates a vast amount of real-time data for the dispatch center, making it challenging for centralized state estimation to meet real-time demands. Objective: This paper proposes a distributed power system state estimation considering dynamic state constraints to address the above issues. Methods: By incorporating the constraints between the dynamic states of the system dynamic components and the bus voltage phasors, the state transition equations for bus voltage phasors are constructed based on the predicted dynamic states and the nodal injection power equations. This allows taking the dynamic model constraints into account when predicting bus voltage phasors. Then, based on the principle of hierarchical coordination and distributed state estimation, the method of estimation-coordination-correction is adopted to acquire system state information quickly and accurately. method: By incorporating the constraints between the dynamic states of the system dynamic components and the bus voltage phasors, the state transition equations for bus voltage phasors are constructed based on the predicted dynamic states and the nodal injection power equations. This allows taking the dynamic model constraints into account when predicting bus voltage phasors. Then, based on the principle of hierarchical coordination and distributed state estimation, the method of estimation-coordination-correction is adopted to acquire system state information quickly and accurately. Results: Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the proposed method. The proposed method is compared with other algorithms to prove its superiority. result: Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the proposed method. And the proposed method is compared with other algorithms to prove its superiority. Conclusion: The simulation results show that the proposed method can effectively improve the accuracy of the state estimation results of power systems under dynamic conditions. conclusion: The simulation results show that the proposed method can effectively improve the accuracy of the state estimation results of power systems under dynamic conditions. other: None

DLOAM: Real-time and Robust LiDAR SLAM System Based on CNN in Dynamic Urban Environments

Dynamic object detection, state estimation, and map-building are crucial for autonomous robot systems and intelligent transportation applications in urban scenarios. Most current LiDAR Simultaneous Localization and Mapping (SLAM) systems operate on the assumption that the observed environment is static. However, the overall accuracy and robustness of a SLAM system can be compromised by dynamic objects in the environment. Aiming at the problem of inaccurate odometry estimation and wrong mapping caused by the existing LiDAR SLAM method which cannot detect the dynamic objects, we study the SLAM problem of robots and unmanned vehicles equipped with LiDAR traveling in the dynamic urban scenes. We propose a fast LiDAR-only model-free dynamic objects detection method, which uses the spatial and temporal information of point cloud through a convolutional neural network (CNN), and the detection accuracy is improved by 35 use spatial information. We further integrate it into a state-of-the-art LiDAR SLAM framework to improve the SLAM performance. Firstly, the range image constructed by LiDAR point cloud is used for ground extraction and non-ground point clustering. Then, the motion of objects in the scene is estimated by the difference between adjacent frames, and the segmented objects are further divided into dynamic objects and static objects by their motion features. After that, the stable feature points are extracted from the static objects. Finally, the pose transformation of adjacent frames is solved by matching feature point pairs. We evaluated the accuracy and robustness of our system on datasets with different challenging dynamic environments, and the results show our system has significant improvements in accuracy and robustness of odometry and mapping, while still maintain real-time performance, which is sufficient for autonomous robot systems and intelligent transportation applications in urban scenarios.

Intraday residual transfer learning in minimally observed power distribution networks dynamic state estimation

Abstract Traditionally, electricity distribution networks were designed for unidirectional power flow without the need to accommodate generation installed at the point of use. However, with the increase in Distributed Energy Resources and other Low Carbon Technologies, the role of distribution networks is changing. This shift brings challenges, including the need for intensive metering and more frequent reconfiguration to identify threats from voltage and thermal violations. Mitigating action through reconfiguration is informed by State Estimation, which is especially challenging for low voltage distribution networks where the constraints of low observability, non-linear load relationships, and highly unbalanced systems all contribute to the difficulty of producing accurate state estimates. To counter low observability, this paper proposes the application of a novel transfer learning methodology, based upon the concept of conditional online Bayesian transfer, to make forward predictions of bus pseudo-measurements. Day ahead load forecasts at a fully observed point on the network are adjusted using the intraday residuals at other points in the network to provide them with load forecasts without the need for a complete set of forecast models at all substations. These form pseudo-measurements that then inform the state estimates at future time points. This methodology is demonstrated on both a representative IEEE Test network and on an actual GB 11 kV feeder network.

Hybrid Physics- and Data-Driven Dynamic State Estimation of Electricity-Heat Integrated Energy Systems

Hybrid Physics- and Data-Driven Dynamic State Estimation of Electricity-Heat Integrated Energy Systems

Toward Data Integrity Attacks Against Distributed Dynamic State Estimation in Smart Grid

With the continuous expansion of the power grid nodes scale, traditional centralized state estimation method shows certain limitations in estimation efficiency and accuracy. Recently, some power grids adopt a distributed state estimation method, in which each partition independently estimates the partial state information by partitioning the entire power system. However, the deviation of the state estimation in certain partition will result in the deviation of the estimation results in the entire power grid system. In this paper, we propose the attack strategy against the distributed state estimation in smart grids from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Moreover, the theoretical analysis of the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations are formalized. The effectiveness of the proposed attack strategy is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of a distributed system can indirectly affect the state estimation results of other partitions and attacking against the measurement value of coordination center can directly threaten the state estimation results of the entire power grid. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes two attack strategies against the distributed state estimation of power grid from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Most of the previous works fail to formalize the state estimation deviation of both partial and entire state estimation results of power grid after the attacker launches the attack against the distributed state estimation. We formalize the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations. The effectiveness of the proposed attack strategy against the state estimation of power grid is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of power grid can indirectly cause the deviation in the state estimation results of entire power grid and attacking against the measurement value of the coordination center can directly threaten the state estimation results of the entire power grid. In conclusion, the proposed attack strategies are helpful for the research community to design detection strategies in a targeted manner and can be conveniently applied to the real-world security management system of smart grid.

Dynamic State Estimation for Multi-machine Power Grids Under Randomly Occurring Cyber-Attacks: A Decentralized Framework

Dynamic State Estimation for Multi-machine Power Grids Under Randomly Occurring Cyber-Attacks: A Decentralized Framework

Comparison of the Accuracy of Generator Dynamic State Estimation Based on the Treatment of the Governor-Turbine Mechanical Power

Comparison of the Accuracy of Generator Dynamic State Estimation Based on the Treatment of the Governor-Turbine Mechanical Power

Intelligent scheduling and optimization of microenergy grid: the application and development of computer technology

Abstract For the intelligent management of micro-energy grid, the limitations of traditional scheduling optimization methods have begun to be highlighted, and computer technology has become a new generation of power system support means. This paper proposes a state estimation method based on smart grid measurement technology, and for the characteristics of micro-energy grid anomaly data, adopts the sampling value detection anti-anomaly data method based on amplitude comparison to estimate the grid dynamic process. Based on the grid state estimation dataset, a smart grid scheduling strategy based on cloud computing is constructed. The conditional value-at-risk of the penalty function of generation cost, grid cost, and motion cost is used as the objective function of the dispatch modeling, and the solution method for the optimization of the value-at-risk model is provided. Finally, an example has been built for this dispatch model to access a simulated micro-energy grid system that contains multiple nodes. The results show that the error between the voltage amplitude obtained from the dynamic state estimation of the micro-energy grid using the method of sampled-value detection against anomalous data and the real value is no more than 0.5%, and the data scheduling error rate of the cloud computing does not fluctuate significantly when dealing with a large number of scheduling data tasks, all of which are controlled to be below 0.0015. The scheduling strategy based on cloud computing has a certain degree of generalization when dealing with random and variable source and load scenarios, which can effectively improve the level of power grid business applications and complete the corresponding intelligent scheduling.

Outlier-Resistant State Estimation for Singularly Perturbed Complex Networks With Nonhomogeneous Sojourn Probabilities.

This study investigates an outlier-resistant state estimation problem for singularly perturbed complex networks (SPCNs) with sojourn probabilities and randomly occurring coupling strengths. Aiming at better describing the dynamic behavior of the network topology for SPCNs, a novel switching law associated with the time-varying sojourn probabilities is developed, and the variation of sojourn probabilities is arranged by a high-level deterministic switching signal. Meanwhile, a sequence of mode-dependent variables is employed to describe the randomly occurring coupling strength. Subsequently, to alleviate the side effects from possible measurement outliers, a dynamic saturation function-based state estimator is designed, whose saturation level is adaptively varying based on previous estimation errors. In virtue of Lyapunov theory and mode-dependent average dwell-time strategy, it can be verified that the resulting dynamics is stochastic H∞ finite-time bounded. To this end, a simulation example is presented to show the validity of the proposed estimator design method.

Dynamic state estimation based protection using multi-section multi-layer frequency dependent line model for underground cables in MMC-HVDC grids

Dynamic state estimation based protection using multi-section multi-layer frequency dependent line model for underground cables in MMC-HVDC grids

Adaptive Divided Difference Filter for Power Systems Dynamic State Estimation With Outliers and Unknown Noise Covariance

Adaptive Divided Difference Filter for Power Systems Dynamic State Estimation With Outliers and Unknown Noise Covariance

Development and Dynamic State Estimation for Robotic Knee–Ankle Orthosis With Shape Memory Alloy Actuators

Abstract The development of rehabilitation robots has long been an issue of increasing interest in a wide range of fields. An important aspect of the ongoing research field is applying flexible components to rehabilitation equipment to enhance human−machine interaction. Another major challenge is to accurately estimate the individual’s intention to achieve safe operation and efficient training. In this article, a robotic knee−ankle orthosis (KAO) with shape memory alloy (SMA) actuators is developed, and the estimation method is proposed to determine the joint torque. First, based on the analysis of human lower limb structure and walking patterns, the mechanical design of the KAO that can achieve various rehabilitation training modes is detailed. Next, the dynamic model of the hybrid-driven KAO is established using the thermodynamic constitutive equation and Lagrange formalism. In addition, the joint torque estimation is realized by the nonlinear Kalman filter method. Finally, the prototype and human subject experiments are conducted, and the experimental results demonstrate that the KAO can assist lower limb movements. In the three experimental scenarios, reductions of 59.1%, 16.5%, and 73% of the torque estimation error during the knee joint movement are observed, respectively.

Inverse-Covariance-Intersection-Based Distributed Estimation and Application in Wireless Sensor Network

In a sensor network, estimation performance may degrade when unknown correlations among local estimates are not addressed carefully. This article presents a novel distributed estimation algorithm based on inverse covariance intersection (ICI) for effectively solving the cross-correlation and dynamic state estimation problems in a wireless sensor network. The intermediate results of a set of consensus filters running in parallel are utilized to realize a global fusion of estimates, which involves all agents' local estimates and improves the estimation accuracy. Meanwhile, the global consistency of the proposed algorithm can be guaranteed theoretically since the fusion process considers all local estimates jointly and in a unified way. Furthermore, besides the asymptotic performance, the boundedness and consistency of the fused estimate can be achieved under finite iterations, which demonstrates its robustness and potential in practical applications. A cooperative target tracking problem illustrates the performance of the proposed algorithm.

Robust dynamic and algebraic state estimation for microgrids: A generalized approach

AbstractNetwork control, optimization, and security analysis are facilitated by accurate microgrid state estimation. By utilizing phasor measurement units (PMUs), a combined robust centralized dynamic algebraic approach is proposed in this paper for estimating algebraic states (voltage phasors) as well as dynamic states (associated with synchronous generators and wind turbines). Assuming that a load is placed on each bus, an innovative PMU placement method is also presented to provide measurements for all dynamic and algebraic state variables. As a robust link between algebraic state estimation and dynamic state estimation, the output results of the least absolute value (LAV) estimator, which is robust against bad data, are fed to dynamic state estimators as pseudo measurements. Next, to address the nonlinearity of synchronous generator and wind turbine models, an unscented Kalman filter (UKF) is applied to estimate dynamic states precisely. The numerical tests on a microgrid test system show that the suggested approach is suitable for both algebraic and dynamic state estimation. Synchronous generators and wind turbines are modeled using nonlinear models of the 9th and third orders, respectively, and static loads are modeled as voltage‐frequency dependent ones.

Accuracy analysis for distributed dynamic state estimation in large-scale systems with a cyclic network graph

Accuracy analysis for distributed dynamic state estimation in large-scale systems with a cyclic network graph