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• 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]