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State estimation is used in power systems to estimate grid variables based on meter measurements. Unfortunately, power grids are vulnerable to cyber-attacks. Reducing cyber-attacks against state estimation is necessary to ensure power system safe and reliable operation. False data injection (FDI) is a type of cyber-attack that tampers with measurements. This paper proposes network reconfiguration as a strategy to decrease FDI attacks on distribution system state estimation. It is well-known that network reconfiguration is a common approach in distribution systems to improve the system’s operation. In this paper, a modified switch opening and exchange (MSOE) method is used to reconfigure the network. The proposed method is tested on the IEEE 33-bus system. It is shown that network reconfiguration decreases the power measurements manipulation under false data injection attacks. Also, the resilient configuration of the distribution system is achieved, and the best particular configuration for reducing FDI attacks on each bus is obtained.

The widespread use of Voltage Source Converter based High Voltage Direct Current Transmission (VSC-HVDC) technology has significantly increased the complexity of grid configuration and operation, which demands higher quality of AC/DC state estimation. The bad data detection and identification plays a vital role in ensuring the accuracy of state estimation outcomes. This paper presents a new approach to bad data in hybrid AC/DC grids based on the combined deep belief network (DBN) and K-means clustering method. First, the DBNs are trained separately for active and reactive power given the characteristics of VSC-HVDC by which the bad data can be detected. Then, an improved K-means is used for clustering the DBN outputs by setting the mean and the number of the samples within the clusters as two metrics for bad data identification. Finally, the case study is performed in a modified IEEE 14-bus system and the results demonstrate the effectiveness of the proposed method in terms of both the accuracy and efficiency. DOI: 10.5755/j01.eie.25.2.23206

The paper deals with Power System State Estimation (PSSE) that plays essential role in modern dispatcher centres. The aim of the paper is to present results of original investigations on change of properties of PSSE when a symmetrical phase shifter is installed in a Power System (PS). The mentioned phase shifter is one of the types of phase shifters. Such the phase shifter is installed in the Polish Power System. In the paper, the following indices characterizing properties of PSSE are considered: the number of iterations in PSSE calculation, the conditionality of the solved equations and accuracy of estimation. The investigations are made using the IEEE 14-bus test system. The properties of PSSE are analysed when there is the phase shifter and when there is not such the device in PS for the polar and rectangular coordinate systems. The investigations are so organized to take into account possibly all space of states of PS. Results of the investigations show that there is noticeable change of the properties of PSSE as an effect of installation of the phase shifter in PS. In the paper, also such the cases are indicated, in which those changes are the smallest. DOI: http://dx.doi.org/10.5755/j01.eie.24.4.21473

Control center of modern power system utilizes state estimation as an important function. In such structures, voltage phasor of buses is known as state variables that should be determined during operation. To specify the optimal operation of all components, an accurate estimation is required. Hence, various mathematical and heuristic methods can be applied for the mentioned goal. In this paper, an advanced power system state estimator is presented based on the adaptive neuro-fuzzy interface system. Indeed, this estimator uses advantages of both artificial neural networks and fuzzy method simultaneously. To analyze the operation of estimator, various scenarios are proposed including impact of load uncertainty and probability of false data injection as the important issues in the electrical energy networks. In this regard, the capability of false data detection and correction are also evaluated. Moreover, the operation of presented estimator is compared with artificial neural networks and weighted least square estimators. The results show that the adaptive neuro-fuzzy estimator overcomes the main drawbacks of the conventional methods such as accuracy and complexity as well as it is able to detect and correct the false data more precisely. Simulations are carried out on IEEE 14-bus and 30-bus test systems to demonstrate the effectiveness of the approach.

There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges in their development, necessitating research in this area. For the safe operation of lithium-ion batteries, state estimation is very significant and battery parameter identification is the core in battery state estimation. The battery management system for electric vehicle application must perform a few estimation tasks in real-time. Battery state estimation is defined by the battery model adopted and its accuracy impacts the accuracy of state estimation. The knowledge of the actual operating conditions of electric vehicles requires the application of an accurate battery model; for our research, we adopted the use of the dual extended Kalman filter and it demonstrated that it yields more accurate and robust state estimation results. Since no single battery model can satisfy all the requirements of battery estimation and parameter identification, the hybridization of battery models together with the introduction of internal sensors to batteries to measure battery internal reactions is very essential. Similarly, since the current battery models rarely consider the coupling effect of vibration and temperature dynamics on model parameters during state estimation, this research goal is to identify the battery parameters and then present the effect of the vibration and temperature dynamics in battery state estimation.

In vitro experiments were conducted in this work to analyze the proliferation of tumor (DU-145) and normal (macrophage RAW 264.7) cells under the influence of a chemotherapeutic drug (doxorubicin). Approximate Bayesian Computation (ABC) was used to select among four competing models to represent the number of cells and to estimate the model parameters, based on the experimental data. For one case, the selected model was validated in a replicated experiment, through the solution of a state estimation problem with a particle filter algorithm, thus demonstrating the robustness of the ABC procedure used in this work. [ABSTRACT FROM AUTHOR]

Parameter identification of linear time invariant (LTI) dynamic system using extended Kalman filter (EKF) are presented in this work. The efficiency of the EKF is demonstrated using non-stationary responses. For this purpose, a three storied steel frame is used in the laboratory and the recorded ground motion is applied to measure the acceleration response at the top. Using these responses, the EKF based predictor-corrector algorithm is applied to identify the model parameters. It has been observed that EKF based identification scheme may converge to different system matrices (i.e. stiffness) for different experimental results of the same structure. However, their eigen values (i.e. natural frequency and mode shape) remain same. [ABSTRACT FROM AUTHOR]

The actual sound environment system exhibits various types of linear and nonlinear characteristics, and it often contains an unknown structure. Furthermore, observations in the sound environment often contain fuzziness due to several causes. In this paper, a method for estimating the specific signal for a sound environment system with unknown structure and fuzzy observation is proposed by introducing a fuzzy theory and a system model of the conditional probability type. The effectiveness of the proposed theoretical method is confirmed by applying it to an actual problem of the sound environment. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 91(9): 45– 53, 2008; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/ecj.10158 Copyright [ABSTRACT FROM AUTHOR]

In this first part of a two-paper set, the operative configuration (OC) is defined as the state (closed/open) of the protective and switching devices installed on the medium voltage distribution network. OC identification is fundamental for the outage management and state estimation of these networks, particularly for those with a low level of real-time supervision and control. In these networks, however, it is difficult to obtain the OC with conventional analytical techniques because of the scarcity or unavailability of monitored devices and measuring points. To overcome this, a novel methodology of inference is presented here. Part I develops a real-time approach based on power flow analysis, fuzzy fault currents calculation and rule-based Type-2 Fuzzy Logic Systems. Part II proposes an extended real-time approach based on fuzzy relation equations and fuzzy abductive inference. The performance of the methodology is evaluated on a real distribution feeder, and results are shown and discussed. [ABSTRACT FROM AUTHOR]

For a multirate system where the output sampling is slower than the input updating, this brief aims at designing filters for fast state estimation in the H2 and H∞ settings. Because of the multirate nature, linear matrix inequality solutions to the design problems involve a nonconvex constraint, which is numerically tackled by the product reduction algorithm. Finally, a design example is given and the effectiveness of the approach is illustrated. [ABSTRACT FROM AUTHOR]

Static state estimation is a fundamental tool for control and monitoring of electrical power systems. Commonly used solution methods such as least squares may be faulty when gross errors are present among the measurements and therefore suitable techniques have been developed to detect and identify bad data. In this work, a robust state-estimation procedure is presented. Similarly to the least median of the squares method, the proposed procedure is based on the idea of evaluating the different state vectors obtained by solving samples of the measurements of dimension equal to the number of the states. The remaining measurements agree or disagree with the solution according to the values of their residuals. The optimal solution is the one with the maximum agreement between the remaining measurements and those in the sample. A genetic algorithm is used instead of a random selection of the samples, to speed up the appearance of the optimal solution. The proposed procedure was implemented and tested with reference to some IEEE test systems. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of reduced-complexity estimation of general large-scale hidden Markov models (HMMs) with underlying nearly completely decomposable discrete-time Markov chains and finite-state outputs. An algorithm is presented that computes O(∊) (where ∊ is the related weak coupling parameter) approximations to the aggregate and full-order filtered estimates with substantial computational savings. These savings are shown to be quite large when the chains have blocks with small individual dimensions. Some simulation studies are presented to demonstrate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

Like all mechanical devices, motors are subject to failures, which can sometimes lead to the shutting down of an entire industrial process. This paper looks at failure predictions in three-phase line-operated induction machines through spectral analysis of electric and electromagnetic signals. Fault characteristics frequencies generated in the estimated and the measured signal spectrum, as a result of mechanical abnormalities such as broken rotor ban, are analyzed. Spectral analyses of simple stator current, of the current's Park vector modulus and of total and partial instantaneous electric powers are considered as external diagnosis. Internal methods of diagnosis are usually based on a mathematical model of the motor. This requires knowledge of the motor's electrical parameters, which are affected by a number of physical phenomena such as temperature variations, skin effects, core losses and saturation. As internal diagnosis, we examine different approaches to the spectral analysis of electromagnetic torque computed by stator and rotor flux estimation. To tills end, the open loop method, the Luenberger observer and the Kalman filter are employed. Finally, experimental results enable us to draw up a table of comparison of internal and external methods in the detection of rotor imperfections, using two criteria under different load levels. [ABSTRACT FROM AUTHOR]

With the development of automation in distribution systems, distribution supervisory control and data acquisition (SCADA) and many automated meter reading (AMR) systems have been installed on distribution systems. Also distribution management system (DMS) have advanced and include more sophisticated analysis tools. The combination of these developments is providing a platform for development of distribution system state estimation (DSE). A branch-current-based three-phase state estimation algorithm for distribution Systems has been developed and tested. This method chooses the magnitude and phase angle of the branch current as the state variables. Because of the limited number of real-time measurements in the distribution system, the state estimator can not acquire enough real-time measurements for convergence, so pseudo-measurements are necessary for distribution system state estimator. The load estimated at every node from the AMR systems is used as a pseudo-measurement for the state estimator. The algorithm has been tested on three IEEE radial test feeders. In addition to this new strategy for DSE, another Sue is meter-placement. This topic includes the type of measurement as well as the location of the measurement. Our results show the impact of these two issues on accuracy. Several general meter rules based on this analysis are outlined. [ABSTRACT FROM AUTHOR]

This paper resulted from the research aimed at introducing modeling and simulation as a major methodological approach for enhancing power engineering education. The reasons for such an approach an explained first. Different options and uses of the modeling and simulation tools are discussed next. Several implementations of the teaching examples ate outlined. The paper ends with the conclusions reached based on the study. [ABSTRACT FROM AUTHOR]

Linear dynamical systems described by finite-difference or ordinary differential equations are considered. The matrix of the system is uncertain or subject to disturbances, and only the bounds on admissible perturbations of the matrix are known. Outer ellipsoidal estimates of reachable sets of the system are obtained and equations describing the evolution of the approximating ellipsoids are derived. An example is presented. [ABSTRACT FROM AUTHOR]

This letter describes a new technique for state estimation observablilty analysis. This technique is computationally efficient and based on calculating the null space of the measurement Jacobian matrix. The technique is illustrated through a clarifying example. Conclusions are duly drawn. [ABSTRACT FROM AUTHOR]