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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. [ABSTRACT FROM PUBLISHER]

This paper is concerned about the divergence of state estimation solution for multi-area power grids. Such cases typically occur due to a single anomaly in the measurement set or network model, yet will cause failure of the state estimator to provide the solution for the entire system. The objective of our study is to develop an approach that will allow detection, identification, and isolation of the anomaly during the state estimation solution so that a large percentage of the system states can still be estimated, while isolating the anomaly that is causing the divergence of the estimator. The proposed approach is described in detail and its effective utilization for a very large utility system is illustrated by simulations. [ABSTRACT FROM AUTHOR]

Recent research has presented a load scaling based real time state estimation (SE) approach for radial and multiphase distribution networks; the approach makes use of successive weighted least squares estimation and power flow to adjust forecasted load values so that they conform with the available real time measurements. This paper extends the load scaling based SE approach along two directions, while maintaining performance that is commensurate with real time computing requirements. First, the approach is extended to allow for meshes that are practically available in some distribution systems; this is achieved by embedding the power flow equations of the nodes that are part of the loops within the load scaling formulation. Second, a sensitivity-based technique is introduced to handle isolated line-to-ground voltage magnitude measurements in radial feeders; this technique is compatible with load scaling and is timely as many utilities have increased the monitoring of voltage measurements at pilot nodes to guard against voltage problems that are associated with distributed generation. The efficiency of the proposed estimator is demonstrated by comparison with the three-phase voltage based normal equations approach that handles both voltage magnitudes and meshes. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Reduced external networks supplied with real-time data are employed in this paper to solve the state estimation problem in interconnected systems. We propose a method to obtain a composed single system state of different monitored areas with local estimations and external equivalent models, which neither require the use of a coordination agent nor requires extensive data interchange. Phasor measurement unit (PMU) measurements taken from system boundaries are used to provide updated equivalent networks as well as an inter-area synchronism. The approach allows testing erroneous external models directly in the state estimation framework. The detectability of bad data in interconnection measurements are also preserved. Tests with the IEEE 118-bus and IEEE 300-bus systems show robustness and accuracy of the model under different area decomposition schemes with low computational requirements compared to similar conventional approaches. [ABSTRACT FROM AUTHOR]

We propose a distributed and a dynamic algorithm for a power system state estimation. We model the dependencies among the state vectors of neighboring areas and among the state vectors at different times using a factor graph. We then derive message update rules and use these rules to implement a sum-product message passing algorithm on the graph. In message passing, neighboring areas exchange messages which represent their beliefs about the unknown state vectors based on all the related measurements. These beliefs are then used to compute the posterior distribution of the power system state. In our paper, we represent the messages using a particle based approximation. Such a particle-based representation provides a simple and a computationally feasible method to update the messages in each iteration. Further, it allows us to model the nonlinearities present in the power system, and hence leads to a better performance accuracy compared with the traditional methods that use linear models. We show the accuracy of the proposed method via numerical simulations using the IEEE 14 and 118 bus systems as examples. [ABSTRACT FROM PUBLISHER]

This paper presents a load estimator for large scale asymmetric distribution networks. It is based on iterating between 1) a weighted least squares (WLS) estimator that attempts to adjust the forecasted load group values to the telemetered measurements and 2) a three-phase load flow employing the Fortescue transformation. The WLS estimation phase explicitly handles Y- and \Delta-connected unbalanced loads and area losses; it can also identify bad measurements based on the estimated load scaling factors. Numerical results show that the proposed load estimator is superior to conventional load allocation based on current balancing, and that its performance is commensurate with the requirements of real-time applications on unsymmetrical radial distribution networks having up to one million nodes. [ABSTRACT FROM PUBLISHER]

This paper proposes a decentralized state-estimation approach that relies on an elaborated instance of the Lagrangian relaxation decomposition technique. The proposed algorithm does not require a central coordinator but just to moderate interchanges of information among neighboring regions, and exploits the structure of the problem to achieve a fast and accurate convergence. Additionally, a decentralized bad measurement identification procedure is developed, which is efficient and robust in terms of identifying bad measurements within regions and in border tie-lines. The accuracy and efficiency of the proposed procedures are assessed by a large number of simulations, which allows drawing statistically sound conclusions. [ABSTRACT FROM PUBLISHER]

This paper presents a new distributed state estimation method for multiarea power systems. Each area performs its own state estimation, using local measurements, and exchanges border information (estimated boundary states and measurements) at a coordination state estimator, which computes the system-wide state. Furthermore, observability and bad data analysis are accomplished in a distributed manner. The proposed method is illustrated with the IEEE 14-bus system. Test results with the IEEE 118-bus system are given. [ABSTRACT FROM AUTHOR]