Multiple gross errors detection, identification and correction in three-phase distribution systems WLS state estimation: A per-phase measurement error approach

Bretãs, Arturo S.; Bretas, N.G.; Braunstein, Sérgio Halpern; Rossoni, Aquiles; Trevizan, Rodrigo D.

doi:10.1016/j.epsr.2017.05.021

Cited by 38 publications

(10 citation statements)

References 20 publications

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“…In order to include the current measurements obtained by µPMUs as well, Method 2 from [23] is adapted in this paper's NLSE measurement models. The procedure for bad data analysis follows [21,[24][25][26].…”

Section: Non-linear State Estimationmentioning

confidence: 99%

μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE

et al. 2021

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Simultaneous real-time monitoring of measurement and parameter gross errors poses a great challenge to distribution system state estimation due to usually low measurement redundancy. This paper presents a gross error analysis framework, employing μPMUs to decouple the error analysis of measurements and parameters. When a recent measurement scan from SCADA RTUs and smart meters is available, gross error analysis of measurements is performed as a post-processing step of non-linear DSSE (NLSE). In between scans of SCADA and AMI measurements, a linear state estimator (LSE) using μPMU measurements and linearized SCADA and AMI measurements is used to detect parameter data changes caused by the operation of Volt/Var controls. For every execution of the LSE, the variance of the unsynchronized measurements is updated according to the uncertainty introduced by load dynamics, which are modeled as an Ornstein–Uhlenbeck random process. The update of variance of unsynchronized measurements can avoid the wrong detection of errors and can model the trustworthiness of outdated or obsolete data. When new SCADA and AMI measurements arrive, the LSE provides added redundancy to the NLSE through synthetic measurements. The presented framework was tested on a 13-bus test system. Test results highlight that the LSE and NLSE processes successfully work together to analyze bad data for both measurements and parameters.

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Section: Non-linear State Estimationmentioning

confidence: 99%

μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE

et al. 2021

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“…It should be noted that in the p.u. system of the balanced power network, the phase and line voltages were equal [38,39].…”

Section: Figurementioning

confidence: 99%

A Unique Electrical Model for the Steady-State Analysis of a Multi-Energy System

2021

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In order to decarbonize the energy sector, the interdependencies between the power and natural gas systems are going to be much stronger in the next period. Thus, it is necessary to have a powerful simulation model that is able to efficiently and simultaneously solve all coupled energy carriers in a single simulation environment in only one simulation step. As an answer to the described computational challenges, a unique model for the steady-state analysis of a multi-energy system (MES) using the electrical analogy approach is developed. Detailed electrical equivalent models, developed using the network port theory and the load flow method formulation, of the most important natural gas network elements, as well as of the linking facilities between the power and natural gas systems, are given. The presented models were loaded up into a well-known software for the power system simulation—NEPLAN. In the case studies, the accuracy of the presented models is confirmed by the comparison of the simulation results with the results obtained by SIMONE—a well-known software for natural gas network simulations. Moreover, the applicability of the presented unique model is demonstrated by the MES security of a supply analysis.

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“…where Y is the bus admittance matrix. The bus admittance matrix is given as Y = G + jB [23,24]. The current vector is defined in (2), whereas (3) defines the voltage magnitude vector [22]:…”

Section: Optimal Power Flowmentioning

confidence: 99%

“…Note that j as a superscript refers to a complex number imaginary part, while r as a superscript refers to the real part. In terms of phasor angles, the real and reactive power at bus n can be expressed as (10) and (11), respectively [12,23,26,27]:…”

Section: Of 18mentioning

confidence: 99%

“…A neural network operates in such a way that when data are presented at the input layer, the neural nodes (which are interconnected via respective weights and bias for each connections) execute some calculations using activation functions in all the successive layers until the input data reach the output nodes that produce the outputs. Typical activation functions used in neural networks include the sigmoid function and the Rectified linear units (ReLu), defined in (22) and (23), respectively [29]:…”

Section: Multilayer Perceptron Neural Network (Mlpnn)mentioning

confidence: 99%

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Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

2019

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In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes.

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Multiple gross errors detection, identification and correction in three-phase distribution systems WLS state estimation: A per-phase measurement error approach

Cited by 38 publications

References 20 publications

μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE

μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE

A Unique Electrical Model for the Steady-State Analysis of a Multi-Energy System

Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

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