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Topology error, a modeling misrepresentation of the power system network configuration, can undermine the quality of state estimation. In this paper, we propose a new methodology for robust power system state estimation (PSSE) modeled by AC power flow equations when there exists a small number of topological errors. The developed technique utilizes the availability of a large number of SCADA measurements and minimizes the $\ell _{1}$ norm of nonconvex residuals augmented by a nonlinear, but convex, regularizer. Representing the power network by a graph, we first study the properties of the solution obtained from the proposed NLAV estimator and demonstrate that, under mild conditions, this solution identifies a small subgraph of the network that contains the topological errors in the model used for the state estimation problem. Then, we introduce a method that can efficiently detect the topological errors by searching over the identified subgraph. In addition, we develop a theoretical upper bound on the state estimation error to guarantee the accuracy of the proposed state estimation technique. The efficacy of the developed framework is demonstrated through numerical simulations on IEEE benchmark systems.

Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.

Conventional algorithms for polygon rasterization are typically designed to maintain non-topological characteristics. Consequently, topological relationships, such as the adjacency between polygons, may also be lost or altered, creating topological errors. This paper proposes a topology-preserving polygon rasterization algorithm to avoid topological errors. Four types of topological error may occur during polygon rasterization. The algorithm starts from an initial polygon rasterization and uses a set of preserving strategies to increase topological accuracy. The count of the four types of error measures the topological errors of the conversion. Topological accuracy is summarized as 1 minus the ratio of actual topological errors to the total number of possible error cases. When applied to a land-use dataset with a data volume of 128 MB, 127,836 polygons, and extending 1352 km2, the algorithm achieves a topological accuracy of more than 99% when raster cell size is 30 m or smaller (100% for 5 and 10 m). The effects of cell size, polygon shape, and number of iterations on topological accuracy are also examined. [ABSTRACT FROM AUTHOR]

State estimation is one of the most important functions in an energy control centre. An computationally efficient state estimator which is free from numerical instability/ill‐conditioning is essential for security assessment of electric power grid. Whereas approaches to successfully overcome the numerical ill‐conditioning issues have been proposed, an efficient algorithm for addressing the convergence issues in the presence of topological errors is yet to be evolved. Trust region (TR) methods have been successfully employed to overcome the divergence problem to certain extent. In this study, case studies are presented where the conventional algorithms including the existing TR methods would fail to converge. A linearised model‐based TR method for successfully overcoming the convergence issues is proposed. On the computational front, unlike the existing TR methods for state estimation which employ quadratic models, the proposed linear model‐based estimator is computationally efficient because the model minimiser can be computed in a single step. The model minimiser at each step is computed by minimising the linearised model in the presence of TR and measurement mismatch constraints. The infinity norm is used to define the geometry of the TR. Measurement mismatch constraints are employed to improve the accuracy. The proposed algorithm is compared with the quadratic model‐based TR algorithm with case studies on the IEEE 30‐bus system, 205‐bus and 514‐bus equivalent systems of part of Indian grid. [ABSTRACT FROM AUTHOR]

This paper introduces the application of state estimation to double bus double breaker distribution substations, which are typically used in the Korean power system, to improve the reliability of substation automation systems. In spite of the recent remarkable progress in the field of sensors and equipments, it is hard to detect device malfunctions or degraded devices automatically in the supervisory monitoring system. The object of the proposed system is not the state estimation itself, but its application to the supervisory detection of the malfunction or degradation of the devices. The supervisory monitoring process consists of a two step procedure—topology processing and normal state estimation. In a substation, the on-off states of the circuit breakers determine the topology. This paper mainly presents the topology processing expert system including the knowledge representation. The expert system is operated with a predefined period to identify and correct topological errors in the distribution substations. The proposed system is now in the field test stage in the Sanchung pilot substation and it will be applied to the next generation substation automation system in Korea. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper we introduce a comprehensive measure of quality of coverage and organization in self-organizing maps. The new measure, named CQoCO, combines self-organizing criteria as outlined by Polani, by measuring how, and where neurons are mapped in space and how they are organized. The result is an all-inclusive tool to account for the required properties of a well-organized SOM—untangled coverage of inputs only, reflecting the density of the input space and outlining the natural clusters in the data. The proposed measure has been tested on the traditional version of SOM as well as on a growing version, as proposed by the authors—ParaSOM. While the patterns are simple, the tests also cover a comparison between CQoCO and the measures of quantization error and topological error. The conclusion becomes evident with the progress of the experiments—CQoCO provides an across-the-board measurement of the quality of coverage and organization in SOMs. [Copyright &y& Elsevier]

Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners. [ABSTRACT FROM AUTHOR]