Hierarchical State Estimation Research Articles

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

Autonomous Hierarchical Surgical State Estimation During Robot-Assisted Surgery Through Deep Neural Networks

Many operations in robot-assisted surgery (RAS) can be viewed in a hierarchical manner. Each surgical task is represented by a superstate, which can be decomposed into finer-grained states. The estimation of these discrete states at different levels of temporal granularity provides a temporal perception of the current surgical scene during RAS, which is a crucial step towards many automated surgeon-assisting functionalities. We propose Hierarchical Estimation of Surgical States through Deep Neural Networks (HESS-DNN), a deep learning-based system that concurrently estimates the current super- and fine-grained states. HESS-DNN incorporates endoscopic vision, robot kinematics, and system events data from the da Vinci Xi surgical system. HESS-DNN is evaluated on a real-world robotic inguinal hernia repair surgery dataset: HERNIA-20, and achieves accurate state estimates of both surgical superstate and the corresponding fine-grained surgical state. We show that HESS-DNN improves state-of-the-art fine-grained state estimation across the entire HERNIA-20 RAS procedure through its hierarchical design. We also analyze the relative contributions of each input data type and HESS-DNN's design to surgical (super)state estimation accuracy.

Asynchronous Hierarchical Forecasting-Aided State Estimator With Sub-Area Data Validation for Power Systems

Estimating the states of a large interconnected power system for monitoring the network, considering its time evolution, is a computationally complex task owing to its large geographical spread. An asynchronous hierarchical forecasting-aided state estimator (AHFASE), which collates the estimates, evaluated by smaller reduced order subsystems to compute the global estimates is proposed in this paper. Individual subsystems process their own local observations in parallel to evaluate the local estimates; thereby reducing the overall computational complexity. Local information from each subsystem is then transmitted to the global estimator, where the incoming information is assimilated to evaluate a near-optimal estimate. The local processors operate asynchronously, transmitting information to the central processor as and when the local observations are processed. The local information arriving at the central processor could become corrupted due to error in communication network or due to some malicious attacks. An asynchronous data validation algorithm is also developed to validate the accuracy of the incoming information. A multi-level architecture of AHFASE is also discussed. The proposed method is computationally efficient, robust to bad data, and resilient to central estimator failure. The accuracy of the developed algorithm is assessed using IEEE 30 and 118-bus test beds.

Privacy-Preserving Hierarchical State Estimation in Untrustworthy Cloud Environments

Hierarchical state estimation (HSE) is often deployed to evaluate the states of an interconnected power system from telemetered measurements. By HSE, each low-level control center (LCC) takes charge of the estimation of its internal states, whereas a trusted high-level control center (HCC) assumes the coordination of boundary states. However, a trusted HCC may not always exist in practice; a cloud server can take the role of an HCC in case no such facility is available. Since it is prohibited to release sensitive power grid data to untrustworthy cloud environments, considerations need to be given to avoid breaches of LCCs' privacy when outsourcing the coordination tasks to the cloud server. To this end, this article proposes a privacy-preserving HSE framework, which rearranges the regular HSE procedure to integrate a degree-2 variant of the Thresholded Paillier Cryptosystem (D2TPC). Attributed to D2TPC, computations by the cloud-based HCC can be conducted entirely in the ciphertext space. Even if the HCC and some LCCs conspire together to share the information they have, the privacy of non-conspiring LCCs is still assured. Experiments on various scales of test systems demonstrate a high level of accuracy, efficiency, and scalability of the proposed framework.

State estimation of 500 kV sulphur hexafluoride high‐voltage CBs based on Bayesian probability and neural network

Circuit breakers (CBs) are of vital importance for the stability of power systems, and a new two-stage hierarchical state estimation method is proposed for 500 kV sulphur hexafluoride CBs based on Bayesian probability and perceptron neural networks. On the basis of the samples collected from Yunnan Power Grid in China, a new indicator system is constructed by association rules. Bayesian probability is applied to measure the correlation between the individual indicators and comprehensive indicators at the same status level, to weigh the individual indicators. Also, an adaptive perceptron is improved to train the weights of comprehensive indicators in different operational conditions, to eliminate the influence of the imbalance problem of relative deterioration. Then, the operating state of equipment can be inferred according to the calculated comprehensive scores. Finally, taking the actual operating equipment as an example, the effectiveness of this proposed method is proved by sample tests and comparison with other existing linear methods.

A hierarchical state estimation and control framework for monitoring and dissolved oxygen regulation in bioprocesses.

The integration of state estimation and control is a promising approach to overcome challenges related to unavailable or noisy online measurements and plant-model mismatch. Extended Kalman filter (EKF) and moving horizon estimator (MHE) are widely used methods that have complementary features. EKF provides fast estimation and MHE optimal performance. In this paper, a novel hierarchical EKF/MHE approach combined with a dynamic matrix controller (DMC), denoted as EKF/MHE-DMC, is proposed for process monitoring and dissolved oxygen control in airlift bioreactors. The approach is successfully tested in simulated cultivations of Escherichia coli for recombinant protein production, considering specific scenarios of step set point tracking, step disturbance rejection, plant-model mismatch, and measurement noise. Results also show that, given a model that describes the measured dissolved oxygen precisely, as assumed in this study for the in silico experiments, the EKF/MHE-DMC approach is able to estimate the cell, protein, substrate, and dissolved oxygen concentrations based only on the measurement of the latter, reducing the estimation error by 93.8% when compared to a benchmark case employing EKF and DMC. The general structure of the proposed EKF/MHE-DMC framework could be adapted for implementation on other relevant bioprocess systems employing their derived process models.

Hierarchical Multi-Area State Estimation via Sensitivity Function Exchanges

A new hierarchical multi-area power system state estimation method is proposed in this paper. Instead of exchanging boundary measurements or state estimates, the proposed technique is based on exchanging the sensitivity functions of local state estimators. The main benefit of the proposed scheme is the improved convergence speed, which also reduces the amount of information exchange required. Extensive numerical results involving IEEE standard systems and a utility scale system are presented.

Fully Distributed State Estimation for Wide-Area Monitoring Systems

This paper presents a fully distributed state estimation algorithm for wide-area monitoring in power systems. Through iterative information exchange with designated neighboring control areas, all the balancing authorities (control areas) can achieve an unbiased estimate of the entire power system's state. In comparison with existing hierarchical or distributed state estimation methods, the novelty of the proposed approach lies in that: 1) the assumption of local observability of all the control areas is no longer needed; 2) the communication topology can be different than the physical topology of the power interconnection; and 3) for DC state estimation, no coordinator is required for each local control area to achieve provable convergence of the entire power system's states to those of the centralized estimation. The performance of both DC and AC state estimation using the proposed algorithm is illustrated in the IEEE 14-bus and 118-bus systems.

Hierarchical state estimation using a fast rectangular-coordinate method (power system analysis computing)

A hierarchical computing scheme for power system state estimation is developed by carefully considering mismatches arising from the system decomposition. The method makes use of an extension of a fast second-order load-flow method that allows a fixed Jacobian matrix to be used in the hierarchical algorithm. In the problem formulation, the power network is decomposed into two or more subsystems; the interaction among them is taken into account through the tie-line bus voltages. The hierarchical structure of the method consists of two levels: the upper level, where the optimal tie-line bus voltages are evaluated; and the lower level, where the optimal states of each subsystem are determined by minimizing a cost function that involves the entire system. Three test cases using the method are reported here and the test results are compared with those obtained by another hierarchical power system state estimation method. >

An Efficient Dynamic State Estimator for Large-Scale Electric Power Systems

Two specific aspects concerning hierarchical dynamic state estimation of electric power systems are considered. The first deals with the exploration of the suboptimality properties of the hierarchical scheme. The second concerns algorithmic improvements by taking into account the Pθ/QV decoupling property of high voltage electric power systems so as to reduce the computational burden of the Kaiman filter. A simple realistic example confirms the theoretical developments and illustrates the effectiveness of the decoupled estimator.

Decentralized Robust Bad-Data Pre-Cleaning with Subsequent Hierarchical Fast Recursive Estimation

In this paper, a new hierarchical static state estimation method for large-scale power system is introduced. For each subsystem and its tie-lines, a robust local bad-data precleaning (BDPC) (detection, identification and correction) is achieved before applying a new fast recursive state estimator (RSSE). The BDPC technique is based on non-linear transformation by which four bad data indicators (RV. Rθ'. RQ. RQI) are defined and used for precleaning bad data in ail available types of measurements. The fast RSSE is based on the direct use of the measurement redundancy together with the network flow equations to achieve a recursive estimate that verifies the network conditions at stationary opertation. A reasonable data exchange between the interconnected subsystems is exploited to handle tie-lines bad-data at the subsystem level. A comparison with a well known algorithm (Kurzyn. 1983. 1987) is given as well. The proposed algorithm have been applied on the AEP 30-bus test system. The results gained on this network demonstrate that the presented technique is a powerful tool for on-line monitoring and control in large-scale power systems.

A New Hierarchical Approach for Dynamic State Prediction and Filtering in Electric Power Systems

Hierarchization is an interesting loophole in the extremely demanding problem of dynamic state estimation of electric power systems, built on an extended Kalman filter and called to work in real time. The first - and up to now only - hierarchical structure developed to this end uses a state variable-wise decomposition principle. This paper tentatively proposes a novel approach, based on a measurement-wise decomposition. If it is too early to make a comprehensive assessment of its advantages and intrinsic characteristics, we can however foresee its complementarity with the former hierarchical dynamic state estimation.

Composite Hydroelectric Energy System State Estimation

The feasibility and performance of a combined eiectrical-mechanical-hydraulic, static-dynamic hierarchical state estimator is investigated. A central tracking electric power system static state estimator co-ordinates ‘local’ voltage and phase information to a series of ‘local’ dynamic hydro turbine state estimators at regular intervals, after each ‘global’ measurement scan. Also present are ‘local’ dynamic hydro canal state estimators. The open channel hydro canal and hydro turbine Kalman filters use a ‘linearized’ approach which involves the least amount of computation yet is stable and capable of tracking changes. The Kalman filter gain matrix of the hydro canal state estimator is relatively insensitive to changes in flow conditions and has a sparse band-like structure.

Hierarchical Dynamic State Estimation in Electric Power Systems

Abstract In many countries, huge power networks are formed-from the interconnection of electric power companies, each one having its own supervisory control center. In order to supervise efficiently such power systems, hierarchical state estimation techniques have to be considered. This paper presents a new algorithm using hierarchical concepts to solve the dynamic state estimation (DSE) problem for large-scale electric power system operating under quasi-static conditions. A two-level calculation is performed to give a satisfactory solution from the computational point of view for the usual steps of the DSE procedure: model parameter estimation, state prediction and state filtering. At the local level, a conventional DSE algorithm is carried out simultaneously and independently for (all subsystems. The coordination of these local estimations is obtained at a national center for supervision and control, which is at the top of operational hierarchy in the interconnected electrical system. Numerical results show the performance of the proposed algorithm under different operational conditions: normal, sudden variation of states and occurrence of bad data.

Real-Time State Estimation for Large-Scale Power Systems

An efficient and simple hierarchical state estimation method, suitable for real-time monitoring of large-scale power systems is investigated in detail. This method exhibits inherent parallelism; it treats all sub-systems of the large system and all tielines coupling these sub-systems separately and independently. Thus, it is very flexible, allowing fast state estimation and bad data processing, the latter being achieved by a specially designed algorithm. Suitability of the method and the algorithm are examined using two 220 kV networks. Several comparisons with the classical, centralised state estimation method are used to illustrate practicality of the hierarchical method.

A Two-Level State Estimator in Electric Power Systems with Bad Data Analysis

We present a hierarchical state estimator appropriate for the monitoring of large-scale electric power systems. Two interesting in practice organization schemes are considered and discussed. The problem of bad data analysis is also investigated; it is shown that the hierarchical approach contributes making this analysis easy, fast and efficient.

Hierarchical static state estimation in a large-scale electric power system: Estimation error analysis

This paper discusses the hierarchical state estimation method for a large-scale power system from the point of view of the estimation error. It is shown that the error is bounded and depends on the number of subsystems comprising the power system, on the subsystem state estimation errors, on the number of measured lines coupling individual subsystems, and on the kind of power flow measurements from those lines used in the calculations. Theoretical considerations are illustrated by use of numerical results.

Hierarchical state estimator: A new way for large power systems estimation

We propose a new concept suitable for performing static state estimation in large scale electric power systems. It consists in decomposing the over-all system into sub-systems and in carrying out a two-level calculation. In a lower hierarchical level, a standard state estimation is at first performed in parallel for all sub-systems and independently from each other. A coordination of these local estimations is then realized at the second (upper) hierarchical level which, by this means, receives and treats only a small number of variables. It may therefore obey various requirements, such as goegraphical, economical, etc. The advantages of this hierarchical procedure over the standard “integrated” one are drawn in the Conclusions.

Recursive hierarchical estimation of the states of an electrical power system

A three-level, hierarchical state estimation method for electrical power systems is proposed, which allows the states of both the network and those of the generating units to be sequentially estimated. The states of the network level are estimated by a two-level, linear, hierarchical, optimal filter, whereas those of the lower generating level by the usual extended, discrete-time Kalman filters. The proposed decentralized method is based on a suitable decomposition of the overall system. Besides having the advantage of using several coordinated estimators of low dimensionality, the described method also provides for a simple network level disturbance detection and localisation procedure. A numerical example illustrates the proposed state estimator.

Corrections to "Identification of Aircraft Stability and Control Parameters Using Hierarchical State Estimation"

Corrections to "Identification of Aircraft Stability and Control Parameters Using Hierarchical State Estimation"