The Largest Normalized Residual Test (LNRT) has been widely utilized in commercial Power System State Estimation (PSSE) software for bad data identification. The LNRT has proved effective in dealing ...with single bad data as well as multiple non-interacting and multiple interacting but non-conforming bad data. However, it is known for a long time that when two bad data are both interacting and conforming, i.e. their errors are in agreement, the LNRT may fail to identify either one. Moreover, it has been shown recently that even two interacting and non-conforming bad data can cause the failure of the LNRT. Drawing on sensitivity analysis in linear regression, we develop normalized deleted residuals for suspected measurements so that the agreement in measurement errors are broken. Therefore, the LNRT for normalized deleted residuals will be able to identify the actual bad data point. Furthermore, in the case of AC PSSE, the method does not require calculation of a new hat matrix when a measurement is deleted from the data set. This makes the method computationally cost-effective. Simulation results for identifying different conforming and non-conforming interacting bad data proves that the proposed method can enhance the effectiveness of the LNRT.
This paper intends to improve the accuracy of power system State Estimation (SE) by introducing a hybrid linear robust state estimator. To this end, automatic bad data rejection is accomplished ...through an M-estimator, i.e. a Schweppe-type estimator with Huber loss function. The method of Iteratively Reweighted Least Squares (IRLS) is used to maximize the likelihood function in the M-estimator. Leverage measurements are also treated by a simple yet effective formulation. To run the algorithm for real-world large-scale grids, cumbersome construction of the Jacobian matrix at each iteration is avoided. In addition, convergence to the local minima faced in the large-scale Gauss-Newton algorithm is not a concern as the proposed formulation is linear with no approximation. As observability and redundancy considerations mandate SE to take advantage of traditional SCADA measurements along with available PMU measurements, the linearity of the proposed SE formulation is guaranteed regardless of whether PMU-only, SCADA-only or hybrid SCADA/PMU measurements are utilized. In this regard, covariance matrix for measurements weights is derived for both types of measurements. Thanks to the linear formulation and therefore swiftness of the proposed algorithm, SE could be run for different power systems with a few up to thousands of buses.
Extra high voltage transmission networks include many untransposed single-circuit overhead lines with horizontal configuration of phase conductors. This paper develops a setting-free fault-location ...method specific to these types of transmission lines. Utilizing synchronized measurements at line terminals, the proposed method accurately locates faults irrespective of fault type, fault resistance, external network parameters, and faulted-line parameters. As such, fault location is robust against ambient conditions as well as inherent inaccuracy of line parameters. The bisymmetry of the impedance matrix of the line is utilized to obtain a closed-form solution for fault location on short transmission lines. For long transmission lines, shunt capacitance of the line is taken into account to obtain an accurate closed-form estimation for fault location. Electromagnetic transient simulation in PSCAD/EMTDC environment validates the accuracy of the proposed fault-location method for a long untransposed 400-kV transmission line. The obtained results are also compared with an existing noniterative parameter-free fault-location method to demonstrate salient features of the proposed method.
The novel Coronavirus disease has increased rapidly in the Wuhan city of China in December 2019. This fatal virus has spread across the whole world like a fire in different stages and affecting ...millions of population and thousands of deaths worldwide. Therefore, it is essential to classify the infected people, so that they can take the precaution in the earlier stages. Also, due to the increasing cases spread of Coronavirus, there are only limited numbers of polymerase change reaction kits available in the hospitals for testing Coronavirus patients. That why it is extremely important to develop artificial intelligence-based automatic diagnostic tools to classify the Coronavirus outbreak. The objective of this paper is to know the novel disease epidemiology, major prevention from spreading of Coronavirus Severe Acute Respiratory Syndrome, and to assess the machine and deep learning-based architectures performance that is proposed in the present year for classification of Coronavirus images such as, X-Ray and computed tomography. Specifically, advanced deep learning-based algorithms known as the Convolutional neural network, which plays a great effect on extracting highly essential features, mostly in terms of medical images. This technique, with using CT and X-Ray image scans, has been adopted in most of the recently published articles on the Coronavirus with remarkable results. Furthermore, according to this paper, this can be noted and said that deep learning technology has potential clinical applications.
Stochastic unary computing provides low-area circuits. However, the required area consuming stochastic number generators (SNGs) in these circuits can diminish their overall gain in area, particularly ...if several SNGs are required. We propose area-efficient SNGs by sharing the permuted output of one linear feedback shift register (LFSR) among several SNGs. With no hardware overhead, the proposed architecture generates stochastic bit streams with minimum stochastic computing correlation (SCC). Compared to the circular shifting approach presented in prior work, our approach produces stochastic bit streams with 67% less average SCC when a 10-bit LFSR is shared between two SNGs. To generalize our approach, we propose an algorithm to find a set of <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula> permutations (<inline-formula> <tex-math notation="LaTeX">n > m > 2 </tex-math></inline-formula>) with a minimum pairwise SCC, for an <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-bit LFSR. The search space for finding permutations with an exact minimum SCC grows rapidly when <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> increases and it is intractable to perform a search algorithm using accurately calculated pairwise SCC values, for <inline-formula> <tex-math notation="LaTeX">n > 9 </tex-math></inline-formula>. We propose a similarity function that can be used in the proposed search algorithm to quickly find a set of permutations with SCC values close to the minimum one. We evaluate our approach for several applications. The results show that, compared to prior work, it achieves lower mean-squared error (MSE) with the same (or even lower) area. Additionally, based on simulation results, we show that replacing the comparator component of an SNG circuit with a weighted binary generator can reduce SCC.
Communities have been exposed to the complications and problems caused by COVID‐19 disease, which has had various and complex effects on general health. The aim of this study was to investigate the ...relationship between anxiety, anger, mindfulness, and general health in the general population during the COVID‐19 outbreak in Iran. This cross‐sectional study was performed on 456 participants from September 2020 to April 2021. For data collection, Demographic Characteristics Form, General Health Questionnaire, Freiburg Mindfulness Inventory‐Short Form, The trait anxiety section of the State‐Trait Anxiety Inventory, The State‐Trait Anger Expression Inventory‐2 were used. General health was positively correlated with anxiety and anger and negatively correlated with mindfulness. Anxiety was positively correlated with anger and negatively correlated with mindfulness. No significant correlation was found between anger and mindfulness. Based on the multiple regression model, anxiety, anger and a family member infected with COVID‐19 were the predictors of general health (p < 0.001). Given that anxiety, anger, and family members infected with COVID‐19 are all predictors of public health, it is suggested that psychological programs and interventions be designed to reduce anxiety and anger, as well as to support family members infected with COVID‐19, to promote general health.
This paper presents a closed-form and non-iterative solution for the long-studied SCADA-based state etimation problem, where unsynchronized traditional measurements from remote terminal units are ...used. In this regard, a novel reformulation of the problem is introduced, where unknowns are expressed as complex variables in terms of unsynchronized SCADA measurements. To this end, it is assumed that bus voltage amplitudes as well as current amplitudes and active and reactive power flows/injections are available. The resulting system of equations is solved by the classic linear weighted least-squares method. In contrast to the traditional approaches, several drawbacks, such as initialization, and issues with convergence (especially for large-scale systems) are resolved, without using synchrophasors. The method is validated on a 3-bus test network and applied to the IEEE 118-bus test system, as well as 1341-bus and 9241-bus European high-voltage transmission networks.
This paper proposes a robust and computationally efficient wide-area backup protection (WABP) scheme against asymmetrical faults on transmission systems using available synchronized/unsynchronized ...phasor measurements. Based on the substitution theorem, the proposed scheme replaces the faulted line with two suitable current sources. This results in a linear system of equations for WABP, with no need of full system observability by measurement devices. The identification of the faulted line is attributed to the sum of squared residuals ( SoSR ) of the developed system of equations. To preserve accuracy, the scheme limits the calculations to the assessment of the negative-sequence circuit of the gird. Relevant practical aspects that have not been properly addressed in the literature, namely the non-simultaneous opening of circuit breakers (CBs) and their single-pole tripping for single-phase to ground faults are investigated. The linearity of the formulations derived removes concerns over convergence speed and potential time-synchronization challenges. The proposed scheme is able to identify the faulted line and retain this capability for hundreds of milliseconds following the fault inception. More than 20 000 simulations conducted on the IEEE 39-bus test system verify the effectiveness of the proposed WABP scheme.
In this paper, a modified frequency control model is proposed, where the demand response (DR) control loop is added to the traditional load frequency control (LFC) model to improve the frequency ...regulation of the power system. One of the main obstacles for using DR in the frequency regulation is communication delay which exists in transferring data from control center to appliances. To overcome this issue, an adaptive delay compensator (ADC) is used in order to compensate the communication delay in the control loop. In this regard, a weighted combination of several vertex compensators, whose weights are updated according to the measured delay, is employed. Generating the phase lead is the most important strategy for these compensators to eliminate the impact of communication delay. Moreover, to overcome the impact of disturbances and uncertainties in power system, an active disturbance rejection control (ADRC) is utilized as the load frequency controller. Being used instead of a PI controller, this robust controller employs an extended state observer to estimate the disturbances and uncertainties and uses a feedback controller to compensate them. The confined iterative rational Krylov algorithm (CIRKA) is employed to reduce the order of the detailed IEEE 39-bus test system model meticulously and facilitate the controller process design. Therefore, a DR control loop, ADC, and ADRC are employed in the LFC to regulate the frequency of the power system in a more efficient way. The simulation results confirm the effectiveness and robustness of the frequency control in presence of communication delay and model uncertainties.
Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are ...vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie–Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.
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