State Estimation Solution Research Articles

A sparse recovery model with fast decoupled solution for distribution state estimation and its performance analysis

This paper introduces a robust sparse recovery model for compressing bad data and state estimation (SE), based on a revised multi-stage convex relaxation (R-Capped-L1) model. To improve the calculation efficiency, a fast decoupled solution is adopted. The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements. The robustness and the computational efficiency of the R-Capped-L1 model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.

Iraqi Super Grid Network State Estimation Using PSO Technique

Power System State Estimation (PSSE) became a main subject in the operation of power systems through its important role in ensuring the secure and economical operation of the power system. In this work, two approaches were proposed and implemented in order to search for the optimal solution of state estimation in power systems, the first approach used a conventional state estimation program based on Weighted Least Square (WLS) method, and the second one used an intelligent technique based on Particle Swarm Optimization (PSO). All programs were implemented using MATLAB and developed to solve the state estimation problem of the Iraqi Super Grid network (400kV). The results showed that the PSO is more accurate and it convergence close to the optimal solution

Partial Diffusion Kalman Filtering for Distributed State Estimation in Multiagent Networks.

Many problems in multiagent networks can be solved through distributed learning (state estimation) of linear dynamical systems. In this paper, we develop a partial-diffusion Kalman filtering (PDKF) algorithm, as a fully distributed solution for state estimation in the multiagent networks with limited communication resources. In the PDKF algorithm, every agent (node) is allowed to share only a subset of its intermediate estimate vectors with its neighbors at each iteration, reducing the amount of internode communications. We analyze the stability of the PDKF algorithm and show that the algorithm is stable and convergent in both mean and mean-square senses. We also derive a closed-form expression for the steady-state mean-square deviation criterion. Furthermore, we show theoretically and by numerical examples that the PDKF algorithm provides a trade-off between the estimation performance and the communication cost that is extremely profitable.

Distributed State Estimation of Multi-region Power System based on Consensus Theory

Effective state estimation is critical to the security operation of power systems. With the rapid expansion of interconnected power grids, there are limitations of conventional centralized state estimation methods in terms of heavy and unbalanced communication and computation burdens for the control center. To address these limitations, this paper presents a multi-area state estimation model and afterwards proposes a consensus theory based distributed state estimation solution method. Firstly, considering the nonlinearity of state estimation, the original power system is divided into several non-overlapped subsystems. Correspondingly, the Lagrange multiplier method is adopted to decouple the state estimation equations into a multi-area state estimation model. Secondly, a fully distributed state estimation method based on the consensus algorithm is designed to solve the proposed model. The solution method does not need a centralized coordination system operator, but only requires a simple communication network for exchanging the limited data of boundary state variables and consensus variables among adjacent regions, thus it is quite flexible in terms of communication and computation for state estimation. In the end, the proposed method is tested by the IEEE 14-bus system and the IEEE 118-bus system, and the simulation results verify that the proposed multi-area state estimation model and the distributed solution method are effective for the state estimation of multi-area interconnected power systems.

An agent-based approach to power system dynamic state estimation through dual unscented Kalman filter and artificial neural network

This paper proposes a novel approach to the power system dynamic state estimation by using the concepts of dynamic artificial neural networks and dual unscented Kalman filter. A dynamic artificial neural network is applied for developing a discrete-time state transition model of power system which is used for one-step-ahead prediction of the state vector. The model is also applicable for the generation of pseudo-measurements while it is needed. The dual unscented Kalman filter estimates the state vector of power system and calculates the weights of the dynamic artificial neural network simultaneously. A multi-agent-based model is utilized to structure the computational architecture of the proposed approach. The autonomous agents are designed to carry out the complex computations needed in the power system dynamic state estimation. The agents distribute multiple execution tasks among themselves, and they share information interactively. The agent-based modeling of the proposed method enhances the computing efficiency through decomposition of the computations among several autonomous agents. The effectiveness of the proposed method is examined through comprehensive simulations. The results guarantee the performance of the proposed approach as a practical solution for the power system dynamic state estimation.

Packet-data anomaly detection in PMU-based state estimator using convolutional neural network

With more phasor measurement units (PMU) being deployed by utilities for reliable monitoring of power systems, there grows an increasing risk of vulnerability to various cyber-attacks. This paper is concerned with a class of false data injection attacks (FDIA) which aim to modify PMU measurements resulting in incorrect state estimation solutions. We extract multi-variate time-series signals from PMU data packets aggregated in phasor data concentrators (PDC) corresponding to different events such as line faults and trips, generation and load fluctuations, shunt disconnections and FDIA prior to every cycle of state estimation (SE). A Convolutional Neural Network (CNN) data filter with Nesterov Adam gradient descent and categorical cross entropy loss is proposed to validate the PMU data. This filter extracts inter time-series relationships to classify different power system events by comparing the temporal structure of PMU packet data. The performance of the filter is then compared with (a) deep learning algorithms such as Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) and (b) traditional classifiers such as SVM and ensemble methods. It is seen that the proposed CNN-based filter results in higher classification accuracies among all classifiers. This makes the CNN classifier suitable to serve as an independent data filter to identify falsified data streams targeted to alter SE. In order to verify the accuracy of the proposed filter, a hybrid state estimator (HSE) has been used in this study which obtains measurements from both PMU and traditional meters. All simulations are carried out on IEEE-30 bus and IEEE-118 bus systems.

A new algorithm for improving the numerical stability of power system state estimation

State estimation (SE) is based on an iterative process for solving weighted least squares (WLS) via the so‐called normal equations (NE). This process is prone to numerical instability and may lead to an ill‐conditioned state estimator in many cases. Although several methods have been proposed in the past to deal with the ill‐conditioning problem in high‐voltage transmission systems, the SE stability of low‐voltage distribution systems remains a challenge due to fewer measurements and high R/X ratios. This paper highlights the main reasons for the ill‐conditioning problem and focuses on the impact of the R/X ratio of the distribution systems. NE‐based SE uses a comparatively simple algorithm with much lower storage size. Hence, this paper proposes a regularized version of the WLS state estimator to solve the problem of ill‐conditioning using an adjustable regularization parameter. Simulation results on U.K. 18‐bus and radial 33‐bus distribution systems show improved performance in terms of reducing the computational complexity, measurements redundancy, the impact of high R/X ratios and improving the accuracy and numerical stability of the SE solution. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Strategic Use of Synchronized Phasor Measurements to Improve Network Parameter Error Detection

The Normalized Lagrange Multiplier (NLM) test is generally effective for network parameter error identification, but suffers occasional detection failures even in the presence of substantial parameter errors. This paper exploits synchronized phasor measurements to address this issue. The parameters whose errors may go undetected are identified, and an index is defined to quantify the power of the NLM test for different parameters. Additional indices to gauge the impact of different parameter errors on the state estimation solution are also derived. Finally, a strategy for using phasor measurement units (PMUs) to avoid parameter error detection failures is presented by formulating an optimization problem using these derived indices. Simulation results are provided to illustrate the benefits brought by the strategic use of PMUs, and to validate the proposed problem formulation and solution.

Robust State Estimation Against Measurement and Network Parameter Errors

Errors in network parameters can cause serious bias in state estimation solution and make good measurements identified and discarded as bad data. This paper addresses this issue by developing a state estimator that remains robust against parameter errors. This is accomplished by modifying the formulation of the well-known least absolute value state estimator in order to identify and reject not only gross measurement errors, but also parameter errors in the network model. The resulting state estimate will not only be free from the impact of erroneous parameters, but will also reliably estimate and correct the erroneous parameters at the same time. The proposed approach can be formulated as a linear programming (LP) problem, and solved in a computational efficient manner by existing LP solvers. Simulation results show that it is effective under different types of parameter errors, gross measurement errors, and Gaussian noise.

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

To address the issue that the phasor measurement units (PMUs) of wide area measurement system (WAMS) are not sufficient for static state estimation in most existing power systems, this paper proposes a mixed power system weighted least squares (WLS) state estimation method integrating a wide-area measurement system and supervisory control and data acquisition (SCADA) technology. The hybrid calculation model is established by incorporating phasor measurements (including the node voltage phasors and branch current phasors) and the results of the traditional state estimator in a post-processing estimator. The performance assessment is discussed through setting up mathematical models of the distribution network. Based on PMU placement optimization and bias analysis, the effectiveness of the proposed method was proved to be accurate and reliable by simulations of different cases. Furthermore, emulating calculation shows this method greatly improves the accuracy and stability of the state estimation solution, compared with the traditional WLS state estimation.

Technical Condition Assessment of Double-Layer Reinforced Concrete Shells of NPP-2006 and NPP-WWER TOI* Project Reactor Compartment

The paper considers a theoretical solution of the technical state estimation of protective hermetic shells. It proposes a solution duplicating the regular system that is independent of the time factor and is intended to determine and assess the stress-strain state of the protective hermetic shells during the acceptance and delivery period. The proposed approach increases the knowledge reliability of the shell current state which increases the NPP operation safety.

OBSERVABILITY ANALYSIS AND STATE ESTIMATION APPROACH OF POWER SYSTEM

This paper introduces a new approach to solve the problem of power system state estimation and check the observability of the measurement scheme. The state estimation is a powerful technique for system monitoring and control. Through a numerical method for observability analysis in power system state estimation based on weighted least square (WLS), the system state will be implemented and the measurement scheme will be judged. In addition to that, investigation for the effects of the measurements redundancy on the accuracy of the estimated variables is also discussed. The technique is applied to IEEE 30 bus system. Test results show the effectiveness of the proposed method to reach the best of the state estimator solution.

Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

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.

Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

Cloud-based IoT solution for state estimation in smart grids: Exploiting virtualization and edge-intelligence technologies

Smart Grids (SGs) are expected to be equipped with a number of smart devices able to generate vast amounts of data about the network status, becoming the key components for an efficient State Estimation (SE) of complex grids. To exploit their potentials, the ICT infrastructure needs to be scalable to follow the increasing amount of data flows and flexible to give the possibility to assign and re-assign grid functions and data flow control policies at runtime, possibly in a context-aware manner. In this scenario, this paper proposes and validates a Cloud-IoT-based architectural solution for SE in SG that combines cloud-capabilities and edge-computing advantages and uses virtualization technologies to decouple the handling of measurement data from the underlying physical devices. Case studies in the field of distribution networks monitoring are also analyzed, demonstrating that the proposed architecture is capable to accomplish the assigned operational tasks, while satisfying the needed quality level from both the communication and the grid perspectives with a significant degree of flexibility and adaptability with respect to state of the art solutions.

5G Mobile Cellular Networks: Enabling Distributed State Estimation for Smart Grids

With the transition toward 5G, mobile cellular networks are evolving into a powerful platform for ubiquitous large-scale information acquisition, communication, storage, and processing. 5G will provide suitable services for mission-critical and real-time applications such as the ones envisioned in future smart grids. In this work, we show how the emerging 5G mobile cellular network, with its evolution of machine-type communications and the concept of mobile edge computing, provides an adequate environment for distributed monitoring and control tasks in smart grids. In particular, we present in detail how smart grids could benefit from advanced distributed state estimation methods placed within the 5G environment. We present an overview of emerging distributed state estimation solutions, focusing on those based on distributed optimization and probabilistic graphical models, and investigate their integration as part of the future 5G smart grid services.

An Efficient and Accurate Solution for Distribution System State Estimation with Multiarea Architecture

Distribution system state estimation (DSSE) is an essential tool for the management and control of future distribution networks. Distribution grids are usually characterized by a very large number of nodes and different voltage levels. Moreover, different portions of the system can be operated by different distribution system operators. In this context, multiarea approaches are key tools to efficiently perform DSSE. This paper presents a novel approach for multiarea state estimation in distribution systems. The proposed algorithm is based on a two-step procedure, where the first-step local estimations are refined through a newly designed second step that allows the integration of the measurement information available in the adjacent areas. The main novelty in this paper is the mathematical analysis of the impact brought by possible measurements shared among different areas, which drives the design of a new efficient weighted least squares formulation of the second step to maximize the achievable estimation accuracy. Tests performed on the unbalanced IEEE 123-bus network prove the goodness of the new multiarea estimator proposed and show the accuracy and efficiency enhancements obtainable with respect to previous literature.

Multiagent System-Based Integrated Solution for Topology Identification and State Estimation

Traditional state estimation (SE) methods are usually implemented in a centralized way. The increased size, complexity of power systems, and the deregulation of the power industries result in the inefficiency of the conventional SE methods. Thus, improved SE solutions in terms of computational speed, easy implementation, and flexibility are urgently needed for future power systems. In this paper, a fully distributed integrated solution is proposed for multiarea topology identification (TI) and SE problems of power systems. Both problems are formulated as a weighted least square problem and solved with a distributed subgradient algorithm via multiagent systems. By applying statistical tests, the TI can identify network topology change accurately. The SE estimates the actual states based on the identified network topology. The proposed distributed solution can be implemented in a fully distributed way based on the flexible decomposition of power systems, and is able to obtain comparable estimated states as centralized methods. Simulation results with IEEE 14-bus and large-scale power systems demonstrate its effectiveness.

PMU-Based Distribution System State Estimation with Adaptive Accuracy Exploiting Local Decision Metrics and IoT Paradigm

A novel adaptive distribution system state estimation (DSSE) solution is presented and discussed, which relies on distributed decision points and exploits the Cloud-based Internet of Things (IoT) paradigm. Up to now, DSSE procedures have been using fixed settings regardless of the actual values of measurement accuracy, which is instead affected by the actual operating conditions of the network. The proposed DSSE is innovative with respect to previous literature, because it is adaptive in the use of updated accuracies for the measurement devices. The information used in the estimation process along with the rate of the execution are updated, depending on the indications of appropriate local metrics aimed at detecting possible variations in the operating conditions of the distribution network. Specifically, the variations and the trend of variation of the rms voltage values obtained by phasor measurement units (PMUs) are used to trigger changes in the DSSE. In case dynamics are detected, the measurement data are sent to the DSSE at higher rates and the estimation process runs consequently, updating the accuracy values to be considered in the estimation. The proposed system relies on a Cloud-based IoT platform, which has been designed to incorporate heterogeneous measurement devices, such as PMUs and smart meters. The results obtained on a 13-bus system demonstrate the validity of the proposed methodology that is efficient both in the estimation process and in the use of the communication resources.

Observer Design for a Bilinear Model of a Continuous Countercurrent ION Exchange Process

The paper describes a method and proposes an algorithm for a bilinear observer design as part of a state estimation solution for a Continuous Countercurrent Ion Exchange (CCIX) process used for desalination of water. The aim of the design is to determine immeasurable process state variables using real CCIX measurement data obtained from a different study. The solution requires determination of the observer gain matrix and it is formulated on the basis of the pole placement method. The contribution of the paper is in the developed bilinear dynamic models of the process and the observer and in the extending of the pole placement method for the special matrices of the bilinear observer. An analytical procedure is developed and implemented for calculation of the characteristic equation of the observer as part of calculation of the observer gain matrix. Matlab and SIMULINK software programs for determining the estimated states are developed. Investigation of the performance of the bilinear observer and the closed loop system under various constant values of the control input and different state initial conditions is performed. The results are presented and discussed.