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Photovoltaic (PV) panels are one of the most emerging components of renewable energy integration. However, where the PV systems bring power conversion efficiency with its bulk installation setup and eco-friendly feasibility, it also brings the factors that could hamper the performance and efficiency of the system. One of these dependent factors is the accumulation of dust particles and its aggregation which could significantly influence the effect of PV systems. The dust can accumulate in its various forms of soiling by fusing with other environmental variations like rain, storm, and humidity. To resolve these challenges which could impact the energy yield of PV systems, the impact of dust as well as effective cleaning mechanisms are required to be studied to restore the performance and power generation output. In this article, an integrated survey of (1) possible factors of dust accumulation, (2) dust impact analysis, (3) mathematical model of dust accumulated PV panels, and (4) proposed cleaning mechanisms discussed in the literature, and (5) a possible sustainable solution for PV systems to survive in this dust accumulated environment are presented. This study also represents handy information to readers, engineers, and practitioners on the development phase of various technologies involved in minimizing the impact of dust accumulation by its removal, adequate cleaning, and restoration. It also addresses the future possible challenges of these cleaning mechanisms. A comprehensive list of publications to date in the open literature is thereby compiled to canvas a complete visual of the different developments in this area. [Display omitted] • PV systems — An emerging component of renewable energy integration and eco-friendly alternative. • Accumulation and aggregation of dust particles on PV panels — A significant influence on the performance. • Dust accumulated PV panels — An integrated survey of factors, mathematical model, and proposed cleaning mechanisms. • Handy information to readers, engineers, and practitioners. • A possible sustainable solution to challenges of water availability and PV systems cleaning mechanisms. [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]

A key component of delivering appropriate autism therapy is the capacity to recognize contributing factors of stimuli manipulation and adjust them as required. Hence, adaptive social psychologists who study autism would emphasize becoming comfortable with the foundations of stimuli manipulation. With a focus on aspects that may be relevant in the implementation of stimulation management systems, this part summarizes a few critical areas of fundamental chemical cognitive behavior investigations in pattern recognition. The bibliography examined here emphasizes behavioral patterns connections across. It covers a variety of subjects, ranging from the breakdown of every component of an n-term conditional to the synthesis of intricate behavioral sequences. Yet, by properly integrating experimental discoveries with implementations in the provision of autism services, this induction and original transitional theory can be improved. The exploratory conduct text provides valuable insights to impart to professionals seeking a deeper comprehension of the fundamentals of restricted conditions that can enhance therapeutic efficacy. Numerous cognitive neuroscience and evolutionary biology investigations have demonstrated that creatures respond to changes in recognized surroundings. Generalisability is a common term for this phenomenon. A drop in reactivity is seen with the preponderance of alterations, although an enhancement is seen with some sensory situations. Both intrinsic and acquired behaviors are included in this. To clarify these events, designers here provide a methodology to restrict conditions or impression selection. Two crucial parts make up the architecture. Then, the sensitivity region is selected as the basic stimulation environment. Every stimulation is reflected by the sequence of sensory apparatus activity it causes. Furthermore, by measuring how response sequences overlap, they offer a straightforward metric for similarity' amongst inspiration in this domain. The real benefit of this strategy is that the generalization of learned behaviors results from a few straightforward rules based on an understanding of how animals probably process inputs. The current paradigm avoids many common issues with sensory theories and models. The degree of applicability across many parameters, the maximum change phenomenon, generalizations according to stimulus intensity, and generalization following conditions on consecutive external emotions represent a few of these concerns. Customers who only selected a special brand degraded any goods bearing a registered trademark from a rival. In summary, these findings are consistent with an interpretation of opinions based on societal programming highlighting the double roles played by popular brands, labeling, and patents. Based on the preceding, we invite scholars to submit original research articles and review papers to the current Special Issue that will focus on learning the principles of stimulus control and analysing human behavior. [ABSTRACT FROM AUTHOR]

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]

The complexity of an interconnected power system has grown exponentially with the increased load demands. These demands and stress conditions are the potential nodes in coupled system to challenge the control and stability of a multi-operational power system. Once these potential nodes are exploited, this could lead to developing stringent conditions and generation of power system oscillations which are required by the grid codes to be damped and controlled adequately. A poorly damped set of power oscillations may lead to system-wide breakups or considerably reduce the power transfer over critical channels and corridors. In recent years, a compelling need has arisen to understand the oscillation damping in the conventional and modern power grids for better performance management, control, and stability in power transfer. In this paper, a bibliographic review on the damping of power system oscillations is provided. The study identifies various control design techniques, technologies, and characteristics used in power systems. Moreover, it discusses various devices and tools to minimize damping during the era of conventional grids and renewable energy integration. The methodology of different approaches and their likely combination are discussed for different types of control design. This study also represents useful information for readers on the complete life-cycle and development phase of technology to minimize the instability variations in generating units, transmission lines, transformers, loads, and HVDC lines. A comprehensive list of publications to date in the open literature is compiled to portray a complete visual of different developments in this area. • Unstable power oscillations result in blackouts and reduced interarea power transfers. • Compel the need to understand oscillation dynamics and damping control in renewable rich grids. • Overview of analysis and control techniques, infrastructure and systems characteristics hitherto. • Classification of past and emerging oscillation frameworks from the perspective of smart grids. • Highlighting technology life cycle and development/deployment phases to minimize instability risk. • Comprehensive list of publications for a visual scenery of hitherto developments in this field. [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 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 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]

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system. • This paper introduces PV inverters and SVC-based reactive power voltage regulation for renewable DG-integrated systems. • Reactive power control in the DG-integrated system is adopted using Reinforcement Learning-Driven PPO. • The validation of the proposed method uses the IEEE-33 bus system to integrate RERs-based DGs and achieve PPO-based control. • The proposed method achieved 68% voltage control, reduced power loss, and reduced the voltage out-of-control ratio to 0.044. • This research compares the suggested approach to different methodologies to demonstrate its validity and applicability. [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]

[Display omitted] • Food waste (FW) contains potential nutrients and compounds for circular bioeconomy. • Efficient management and valorization of FW is the most sustainable method. • Artificial Intelligence (AI) application could help in optimizing FW supply chain. • Machine learning (ML) can predict quite exactly the quantity and composition of FW. • Combination of AI and ML could monitor and manage well FW-based bioprocess. Food waste (FW) is a severe environmental and social concern that today's civilization is facing. Therefore, it is necessary to have an efficient and sustainable solution for managing FW bioprocessing. Emerging technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) are critical to achieving this, in which IoT sensors' data is analyzed using AI and ML techniques, enabling real-time decision-making and process optimization. This work describes recent developments in valorizing FW using novel tactics such as the IoT, AI, and ML. It could be concluded that combining IoT, AI, and ML approaches could enhance bioprocess monitoring and management for generating value-added products and chemicals from FW, contributing to improving environmental sustainability and food security. Generally, a comprehensive strategy of applying intelligent techniques in conjunction with government backing can minimize FW and maximize the role of FW in the circular economy toward a more sustainable future. [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]