Search

Hybrid electric vehicle technology (HEVT) is emerging as a reliable alternative to reduce the constraints of battery-only driven pure electric vehicles (EVs). HVET utilizes an electric motor as well as an internal combustion engine for its operation. These components would work on battery power and fossil fuels, respectively, as a source of energy for vehicle mobility. The power is delivered either from battery or fuel or both sources based on user requirements, road conditions, etc. HEVT uses three major propelling systems, namely, front-wheel drive (FWD), rear-wheel drive (RWD), and four-wheel drive (4WD). In these propelling systems, the 4WD model provides torque to all four wheels at the same time. It uses all four wheels to propel thereby offering better driving capability, better traction, and a strong grip on the surface. The 4WD-based HEVs comprise four architectures, namely, series, parallel, series-parallel, and complex. The literature focuses primarily on any one type of architecture for analysis in the context of component optimization, fuel reduction, and energy management. However, a focus on dynamic analysis that gives a real performance insight was not conducted, which is the main motivation for this paper. The proposed work provides an extensive critical performance analysis of all four 4WD architectures subjected to various dynamic operating conditions (continuous, pulse, and step-up accelerations). Under these conditions, various performance parameters such as speed (of vehicle, engine, and motor), power (of engine and battery), battery electrical losses, charge patterns, and fuel consumption are measured and compared. Further, the 4WD architecture performance is validated with FWD and RWD architectures. From MATLAB/Simulink-based evaluation, 4WD HEV architectures have shown superior performance in most of the cases when compared to FWD type and RWD type HEVs. Moreover, 4WD parallel HEV architecture has shown superior performance compared to 4WD series, 4WD series-parallel, and 4WD complex architectures. [ABSTRACT FROM AUTHOR]

The rapid introduction of electric vehicles (EVs) in the transportation market has initiated the concept of vehicle-to-grid (V2G) technology in smart grids. However, where V2G technology is intended to facilitate the power grid ancillary services, it could also have an adverse effect on the aging of battery packs in EVs. This is due to the instant depletion of power during the charge and discharge cycles, which could eventually impact the structural complexity and electrochemical operations in the battery pack. To address this situation, a median expectation-based regression approach is proposed for parameter estimation of vehicle batteries in V2G systems. The proposed method is built on the property of uncertainty prediction of Gaussian processes for parameter estimation while considering the cell variations as an exogenous function. First, a median expectation-based Gaussian process model is derived to predict the fused and individual cell variations of a battery pack. Second, a magnitude-squared coherence model is developed by the error matrix to detect and isolate each variation. This is obtained by extracting the cross-spectral densities for the measurements. The proposed regression-based approach is evaluated using experimental measurements collected from lithium-ion battery pack in EVs. The parametric analysis of the battery pack has been verified using D-SAT Chroma 8000ATS hardware platform. Performance evaluation shows an accurate estimation of these dynamics even in the presence of injected faults. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. However, the changes and developments in the IoT environment are making IoT systems susceptible to cyber attacks which could possibly lead to malicious intrusions. The impacts of these intrusions could lead to physical and economical damages. This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. Learning-based methods are reviewed using different types of cyber attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. A comprehensive list of publications to date in the literature is integrated to present a complete picture of various developments in this area. Finally, future research directions are also provided in the paper. [ABSTRACT FROM AUTHOR]

The photovoltaic (PV) cells are highly sensitive to temperature variations. The linear variation of PV cells with operating temperature could lead to the loss of conversion efficiency and permanent damage of PV material. To address this critical issue, while improving the performance of solar photovoltaic system (SPVS) during the operating period, the deployment of a proper cooling system is a prime requisite in the design process. Considering this, two new and cost‐effective cooling techniques are proposed in this study, which can improve efficiency and increase the life of the PV panel. The first technique is the active cooling design with water. The proposed design enhances the access to PV cells to maximize cooling. The second one is an improved passive cooling technique that uses fins mounted on the backside of PV module. This further reduces the temperature by increasing the heat dissipation. Additionally, a third PV module without any cooling system is deployed as a reference margin. The techniques have been experimentally evaluated and a recommendation is made in order to reduce the temperature variation across the PV panel. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid (V2G) technology enables bidirectional charging of electric vehicle (EV) and facilitates power grid ancillary services. However, battery pack in EV may develop in-cell dynamic variations over time. This is due to the structural complexity and electrochemical operations in the battery pack. These variations may arise in V2G systems due to: first, additional charging and discharging cycles to power grid; second, external shocks; and third, long exposures to high temperatures. A particular source of these variations is due to faulty sensors. Therefore, it can be argued that the battery packs in EV are highly reliant on the monitoring of these in-cell variations and their impact of propagation with each involved component. In this article, a prediction-based scheme to monitor the health of variation induced sensors is proposed. First, a propagation model is developed to predict the in-cell variations of a battery pack by calculating the covariance using a median-based expectation. Second, a hypothesis model is developed to detect and isolate each variation. This is obtained by deriving a conditional probability-based density function for the measurements. The proposed monitoring framework is evaluated using experimental measurements collected from Li-ion battery pack in EVs. The in-cell variation profiles have been verified using D-SAT Chroma 8000ATS hardware platform. The performance results of the proposed scheme show accurate analysis of these emerged variations. [ABSTRACT FROM AUTHOR]

The deployment of advanced sensors has strengthened the monitoring capability of power plants. In the context of the cogeneration process, the plant cooling is performed by the cooling towers using the condensation process on exhaust steam. However, the computer networks and industrial control systems built on this sensor-based digital layer may become vulnerable to cyberattacks. This may eventually raise a concern on the performance and security of these energy utilities. To resolve this issue, an inoculated subobserver-based fusion filter is proposed. It improves the resilience against malicious attacks in combined cycle power plants with desalination units, which are usually functioning in a closed-loop environment and infected with injected attacks. A time-delay-based state representation is considered for the system. To access latency in a closed-loop environment, a subsystem-based set of subobservers are introduced. Information from each sensor is then gathered using the interacting-multiple-model-based fusion process. The stability of the system has been proved and kept in-check at two different implementation levels of the procedure using functional equivalence and Lyapunov stability criteria, respectively. Performance evaluation is then conducted on a water-level system. Results show that the proposed scheme accurately extracted the system parameters from the contaminated measurements in the presence of multiple system disturbances and cyber intervention. [ABSTRACT FROM AUTHOR]

Monitoring and control of electrical power grids are highly reliant on the accuracy of the digital measurements. These digital measurements reflect the precision of the installed sensors, which are vulnerable to the injection of unknown parameters in the form of device malfunction and cyberattacks. This may question the operational security and reliability of many cyberphysical infrastructure such as smart grid. To resolve this issue, a multisensor temporal prediction based wide-area control scheme is proposed in this paper. The feasibility of the designed scheme is verified in an advanced synchrophasor measurements based wide-area monitoring and control system (WAMCS). This WAMCS adopts a flexible ac transmission system device (the primary controller) for controlling the smart grid's voltage profile. The algorithm is validated in a real-time environment with an innovative software-in-the-loop testing setup. The performance of the proposed technique in the presence of false-data-injection attacks shows promising results. [ABSTRACT FROM AUTHOR]

Utilization of synchrophasor measurements for wide-area monitoring applications enables system operators to acquire real-time grid information. However, intentional injections of false synchrophasor measurements can potentially lead to inappropriate control actions, jeopardizing the security, and reliability of power transmission networks. To resolve this issue, a multisensor track-level fusion-based model prediction (TFMP) has been proposed. It has been demonstrated on a mature wide-area monitoring application, which detect electromechanical oscillations. In this paper, to extract the initial correlation information about attacked oscillation parameters, Kalman-like particle filter (KLPF)-based smoother has been used at each monitoring node. To reduce its computational burden, the KLPF-based smoother is diagonalized into subsystems. The scheme is further supported by the characteristics of moving horizon estimates for handling continuous load fluctuations and perturbations caused by data injections in power grids. Performance evaluations are conducted using different data-injection scenarios in the IEEE New England 39 Bus system. Results show the proposed TFMP accurately extracted oscillatory parameters from the contaminated measurements in the presence of multiple system disturbances and random data injections. [ABSTRACT FROM PUBLISHER]

Monitoring critical infrastructures is highly dependent on the accuracy of the installed sensors and the robustness of estimation algorithms. Data-injection attacks can degrade the operational reliability and security of any cyber-physical infrastructures. An attacker can compromise the integrity of the monitoring algorithms by hijacking a subset of sensor measurements and sending manipulated readings. Such approach can result in wide-area blackouts in power grids. This paper considers several cases of severe data-injections with high probabilities of information loss. To achieve an accurate supervision, a Bayesian-based approximated filter (BAF) has been proposed at each monitoring node using a distributed architecture. To maintain a reduced communication overhead and time complexity, upper and lower bound methods have been developed. The performance of the proposed technique has been demonstrated in a mature synchrophasor application known as the oscillation detection. Two test cases have been generated to examine the immunity of the proposed estimation scheme in New Zealand and Oman power grids. The tests were conducted in the presence of harsh data-injection attacks and multiple system disturbances. Results show the proposed BAF method can accurately extract the oscillatory parameters from the contaminated measurements. [ABSTRACT FROM PUBLISHER]

Lightly damped electromechanical oscillations are major operating concerns if failed to be detected at an early stage. This paper improved the existing extended complex Kalman filter (ECKF) technique of tracking electromechanical oscillations using synchrophasor measurements. The proposed algorithm adopted a distributed architecture for estimating oscillatory parameters from local substations. The novelty lies in handling maximum likelihood (ML) to enhance the convergence property in tracking multiple modes using an expectation maximization (EM) approach. This was achieved by encapsulating the augmented Lagrangian (AL) in the maximization step of the EM algorithm, which utilized a novel ECKF-based smoother (ECKS). Performance evaluations were conducted using IEEE 68-bus system and recorded synchrophasor measurements collected from the New Zealand grid. Random noise variance test cases were generated to examine the performance of the proposed algorithm. To ensure the robustness to random noisy conditions, the algorithm was tested based on exhaustive Monte Carlo simulations. Comparisons were made with the existing Prony analysis (PA), Kalman filter (KF), and distributed EM-based FB-KLPF. [ABSTRACT FROM AUTHOR]

This paper improves the existing Kalman-based technique for detecting electromechanical oscillations using Synchrophasor measurements. The novelty is the utilization of a distributed architecture to extract maximum a-posteriori (MAP) estimations of oscillatory parameters. This was achieved by an expectation maximization (EM) algorithm. To improve initial condition estimation, initial correlation information through a forward backward (FB) Kalman-like particle filter (KLPF) was integrated into the proposed scheme. Performance evaluation was conducted using IEEE New England 39-Bus system and Synchrophasor measurements collected from New Zealand Grid. The proposed method accurately extracted oscillatory parameters when the measurements were contaminated by continuous random small load fluctuations. The method also improved the capability of detecting multiple oscillations with similar frequencies. [ABSTRACT FROM PUBLISHER]

In this paper, a distributed estimation algorithm using Bayesian‐based forward backward Kalman filter (KF) is proposed for stochastic singular linear systems. The method incorporates generalised versions of KF for bounded cases with complete and incomplete prior information, followed by estimation fusion of these cases. The incorporated filters remain optimal given the cross‐covariance of the local estimates. The proposed approach is validated on a coupled‐tank system. [ABSTRACT FROM AUTHOR]

In this paper, a fault-tolerant control scheme is proposed for interconnected systems within an integrated design framework to yield timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is done for the local faults. This critical information is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using these parameter estimates of decentralized subsystems, a model predictive control adjusts its parameters according to the fault scenarios, thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors with a recycle and quadruple tank system indicate that the proposed method is capable of correctly identifying various faults, and then controlling the system under some conditions. [ABSTRACT FROM PUBLISHER]

In this study, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time‐variant physical variables. The main contribution of this work is that the algorithm in Saber and Murray (2005), Carli et al. (2009) and Xiao et al. (2004) has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimised, thereby improving the filter design considerably and characterising the performance limit of the filter, and thereby tracking a time‐varying signal. Moreover, certain parameter optimisation is alleviated with the application of a particular finite impulse response filter. Simulation results are showing the effectiveness of the developed estimation algorithm. [ABSTRACT FROM AUTHOR]

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this study, a bibliographical review on distributed Kalman filtering (DKF) is provided. The study contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area. [ABSTRACT FROM AUTHOR]

In this paper, an efficient scheme has been proposed to model, detect and classify the fault. The modeling of fault has been proposed with the fuzzy logic using membership function. Fault detection of the unprecedented changes in system reliability and find the failed component state by classifying the faults is proposed using kalman filter and hybrid neurofuzzy computing techniques respectively. A fault is detected whenever the moving average of the Kalman filter residual exceeds a threshold value. The fault classification has been made effective by implementing a hybrid genetic neuro-fuzzy Inference system (GANFIS). By doing so, the critical information about the presence or absence of a fault is gained in the shortest possible time, with not only confirmation of the findings but also an accurate unfolding-in-time of the finer details of the fault, thus completing the overall fault diagnosis picture of the system under test. The proposed scheme is evaluated extensively on a two-tank process used in industry exemplified by a benchmarked laboratory scale coupled-tank system. [ABSTRACT FROM AUTHOR]

The efficiency of a photovoltaic (PV) panels drops significantly in dusty environments. The variation in temperature could have a substantial impact on PV panel cells, which could further lead to high deterioration and eventually permanent damage to the PV material in the presence of dust. To resolve this issue, in this work a novel hydrophobic silicon dioxide (SiO2)-based nanoparticle coating is proposed for the PV panel, to shrink the surface stress developed between the water and the coated facet. Two identical PV modules were installed to conduct comparable experimental tests simultaneously. The first module is coated by the SiO2 nanoparticles, and the second is uncoated and used as a reference. To maintain coherency, the experiments are done in the same environmental conditions, cleaning the PV modules at regular intervals. Results reveal that the accumulated energy generated during this period of study was comprehensively enhanced. Moreover, the self-cleaning property of the hydrophobic surface of the coated panel allowed water droplets to slide smoothly down the PV module surface, carrying dust particles. Useful recommendations are made at the end to enhance the performance of PV panels in dusty environments. [ABSTRACT FROM AUTHOR]

The generation of the mix-based expansion of modern power grids has urged the utilization of digital infrastructures. The introduction of Substation Automation Systems (SAS), advanced networks and communication technologies have drastically increased the complexity of the power system, which could prone the entire power network to hackers. The exploitation of the cyber security vulnerabilities by an attacker may result in devastating consequences and can leave millions of people in severe power outage. To resolve this issue, this paper presents a network model developed in OPNET that has been subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an international electrotechnical commission (IEC) 61850 based digital substations. The attack scenarios have exhibited significant increases in the system delay and the prevention of messages, i.e., Generic Object-Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV), from being transmitted within an acceptable time frame. In addition to that, it may cause malfunction of the devices such as unresponsiveness of Intelligent Electronic Devices (IEDs), which could eventually lead to catastrophic scenarios, especially under different fault conditions. The simulation results of this work focus on the DoS attack made on SAS. A detailed set of rigorous case studies have been conducted to demonstrate the effects of these attacks. [ABSTRACT FROM AUTHOR]