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This paper presents an analytical methodology for smart grids cyber-physical security based on gross error analysis. The presented methodology is built on the Weighted Least Square (WLS) state estimator (SE) formulation. Although cyber-physical security is a wide subject, in this paper Cyber-Attacks are modeled as bad data. Detection, identification and correction of multiple and simultaneous cyber-attacks on power grid's SCADA system, or network database, are investigated. Cyber-attack detection is made through a Chi-square ( χ 2 ) Hypothesis Testing (HT) applied to the composed measurement error ( CME ). Composed errors are estimated with measurements’ innovation index ( II ). Cyber-attack identification is made through the Largest Normalized Error Test property. Cyber-attack correction is made considering cyber-attack type and using the composed normalized error ( CNE ). One important advantage of the presented method is it does not require a previous knowledge of how the attack was performed, as far as it is restricted to a change of measurements, parameters or topology, since the error is estimated and then the bad data is corrected. A significant advantage of this correction is that it avoids potential local or global unobservable conditions, since it does not delete any measurement of the measurement set. Validation of the proposed methodology is made on the IEEE 14-bus and 57-bus systems. Simulations show the reliability and robustness of the proposed methodology even when the cyber-attack occurs simultaneously on SCADA data and network database. [ABSTRACT FROM AUTHOR]

High impedance fault location still represents a challenge for protection engineers. In this work, an analytical formulation for high impedance fault location in power distribution systems is presented, based on system parameters estimation. The proposed method is based on a frequency domain system model, developed considering a high impedance fault model composed by two antiparallel diodes and an arc resistance. The proposed model is an overdetermined nonlinear algebraic system of equations in which the solution is obtained through a Weighted Least Square approach. A residual analysis is applied for estimate selection. The proposed method considers the capacitive effect through an exact line segment model for overhead distribution lines, using only local voltages and currents as input data. Validation is done through numerical simulations using the IEEE system 13 buses test feeder, a real distribution system from the RGE Sul Power Utility (Southern Brazil), and a real high impedance fault recorded from a digital relay. Comparative test results with the state-of-the-art highlights the method's high accuracy, indicating potential aspects for real life application. Overall average errors of 0.3% considering a complete HIF model were obtained, and a 1.39% error considering a real HIF in a rural feeder. The formulation is currently used by protection engineers at RGE Sul as a reliability oriented tool. [ABSTRACT FROM AUTHOR]

• Unbalanced parameter correction model against false data injection attacks. • Cyber-physical security framework for bad data analysis of FDI attacks. • Real-time multiple simultaneous measurement and parameter false data injection processing. This paper presents a correction model for malicious, unbalanced parameter false data injection cyber-attacks. Current state-of-art solutions can detect, identify and correct balanced parameter false data injection cyber-attacks. Thus, they consider all the parameters as equal in error, which means the methods will only work when the same percentage attack happens to each parameter. In this paper, a new correction model using a parameter correction Jacobian matrix, τ , and a Taylor series approximation is presented. A framework for measurement gross error analysis is deployed in processing and analyzing cyber-attacks. Chi-square χ 2 Hypothesis Testing applied to the normalized composed measurement error (CMEN) is considered for cyber-attacks detection, while the largest CMEN error test is used for identification. Validation is performed on the IEEE 14-bus and 118-bus systems. Easy-to-implement model, without hard-to-design parameters, built on the classical weighted least squares solution, highlights potential aspects for real-life implementation. [ABSTRACT FROM AUTHOR]

• Identification of vulnerable NTL system areas based on concepts of measurement's innovation and n-tuple of critical measurements. • Synthetic measurement model for GE analysis enhancement. • Framework for NTL estimation in unbalanced distribution networks. This paper presents a hybrid data-driven physics model-based framework for distribution networks nontechnical power loss estimation. Nontechnical power loss is defined as energy delivered to the consumers but not billed by the utility. These losses, unlike technical losses, are not inherent to the process of transportation of electricity. State-of-the-art solutions for nontechnical power loss estimation are either data-driven or physics model-based. However, due to the evolving nature of nontechnical power losses, data-driven solutions by themselves are not sufficient. Physics model-based analytical solutions, otherwise, which consider a quasi-static system model, rely solely on physics phenomena observation, however it is virtually impossible to model all grid dynamics. In this case, the nexus of data-driven physics model-based analytic models enable the solution of the problem. The hybrid framework is composed of three interdependent processes. First, an unbalanced load flow analysis is performed to obtain an initial estimate of the operating system state. Second, a data-driven method for consumer classification is applied. Third, synthetic measurements are created considering the measurement's innovation and n -tuple of critical measurements aiming to improve gross error analysis. Solution validation is made considering the IEEE 4-bus, 13-bus and 123-bus unbalance test feeders. Comparative test results highlight decreased nontechnical power loss estimation errors. Simplicity of implementation, with easy-to-obtain parameters, built on the classical weighted least squares state estimator, indicate potential aspects for real-life applications. [ABSTRACT FROM AUTHOR]

• Comprehensive physics-based analytical model for HIF location. • Statistical behavior of estimates is analyzed to identify the faulted section. • One-ended data based frequency domain measurement model. This paper presents a physics-based analytical model for high impedance fault location in power distribution systems. The presented analytical solution is composed by two interdependent processes. First, fault distance is estimated through a Weighted Least Square approach applied to solve an overdetermined and nonlinear algebraic system of equations. Second, an analytical method based on the statistical behavior of the estimates obtained from the frequency domain system model solution is presented to address the multiple estimates problem, identifying the faulted section. The presented fault location physics-based analytical model considers the capacitive effect of distribution lines. Furthermore, voltages and currents measured from only one terminal are used. The formulation is evaluated using the IEEE 13-bus and a modified version of the IEEE 34-bus test feeders. Comparative analysis with state-of-the-art methods is presented. Average errors of 1.95% are obtained. Easy to implement analytical model, without hard to derive parameters, built on the weighted least squares state estimator, highlight potential aspects for real-life applications. [ABSTRACT FROM AUTHOR]