Kishore Bingi

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• Doctor of Philosophy
• Senior Lecturer at Universiti Teknologi Petronas

This chapter presents the forecasting of wind power generation from wind turbines in Texas, using developed FONN models. The collected dataset contains 8760 samples, which include the essential parameters of wind speed, direction, air temperature, and pressure. Using both the single and multi-layer FONN models evaluates the efficiency of convention...

Reliable and efficient operation of modern power systems is crucial, especially with integrating renewable energy sources, energy storage systems, and advanced monitoring devices. Accurate forecasting is necessary to prevent grid instabilities, blackouts, and equipment failures. To address these challenges, advanced forecasting techniques, such as...

This chapter presents the incorporation of fractional calculus into neural network activation functions to enhance performance. Traditional activation functions like Sigmoid, Tansig, and ReLU are crucial for introducing non-linearity in neural networks, but they have limitations, such as vanishing gradients, non-differentiability, and inactive neur...

Wind power generation forecasting plays a pivotal role in optimizing renewable energy resources. This chapter presents a detailed analysis of predicting wind power output from the Jeju Island wind farm’s three distinct sites (Sites A, B, and C) using FONN models. Employing single-layer and multi-layer FONN models explores the effectiveness of fract...

Renewable energy plays a crucial role in reducing carbon emissions and mitigating the impacts of climate change. Wind energy has emerged as a critical contributor to the sustainable energy transition among various renewable sources. However, integrating electric power grids can be difficult because they are unstable due to multiple factors, includi...

Renewable energy sources like wind and solar irradiance are essential for addressing global energy demands and environmental challenges. However, their inherent variability and intermittency pose significant difficulties in forecasting and grid integration, demanding advanced predictive models. This book proposes fractional-order neural networks an...

Implementing FPGA-based fractional-order controllers in real-time applications can be challenging due to their memory dependencies. These dependencies require high-order integers, leading to several design complications. Issues such as large design sizes, space exploration, and difficulty creating configurable hardware can affect resource utilizati...

Ensuring the reliability of induction motors is essential for industrial applications, as motor failures can lead to unplanned downtime and significant financial losses. Motor current signature analysis (MCSA) has emerged as an effective and non-intrusive technique for diagnosing motor health, particularly for monitoring bearing conditions, which a...

This paper presents a state feedback H ∞ control design for active suspension systems with time-delays using an asymmetric Lyapunov–Krasovskii functional (LKF). Less conservative stabilization conditions are derived in this paper for the design of a state feedback control synthesis through asymmetric LKF in the linear matrix inequalities (LMIs). Th...

This study presents an AI-driven drone inspection system for rooftop solar PV panels, employing the DJI Mavic Mini drone and YOLOv11 AI model to streamline defect detection. The methodology involved capturing high-resolution images via autonomous drone missions, annotating data with RoboFlow, and training the YOLOv11 model to accurately identify de...

This study presents a YOLO-powered vehicle detection and tracking system integrated with a Tello quadrotor drone, addressing the limitations of conventional detection systems. Traditional methods often generalize vehicles into a single category, overlooking their specific operational needs and urgency levels. Furthermore, stationary and GPS-based t...

Fractional-order electrical circuits are a fascinating and innovative field of study that has gained considerable attention recently. Instead of traditional integer-order derivatives, these circuits use fractional-order derivatives, which offer several advantages, including increased accuracy, improved control, and better efficiency. Fractional-ord...

Fractional-order systems are receiving much attention because they can accurately model complex physical phenomena. These systems are more versatile than their real-order counterparts, making them ideal for system modelling. However, designing and implementing fractional-order dynamical systems with complex orders can take time due to the need for...

In recent years, machine learning has become an essential technology across multiple areas, particularly in object detection systems, where it enhances accuracy and automation. This paper critically examines the object detection techniques developed between 2020 and 2024, focusing on three popular methods, namely You Only Look Once (YOLO), Single S...

This paper aims to improve defect identification, operational efficiency, and cost-effectiveness of drone-based photovoltaic (PV) solar panel inspection methods by leveraging artificial intelligence (AI) algorithms and modern imaging technologies. As the usage of renewable energy sources develops, it is vital to maintain solar panels in a timely an...

Drones equipped with real-time object detection offer a novel perspective on our environment, yet they encounter challenges related to hardware limitations and the demands of complex applications. These challenges necessitate innovative solutions, particularly given the potential benefits in areas such as search and rescue operations, agriculture,...

This book suggests the development of single and multi-layer fractional-order neural networks that incorporate fractional-order activation functions derived using fractional-order derivatives. Activation functions are essential in neural networks as they introduce nonlinearity, enabling the models to learn complex patterns in data. However, traditi...

Mobile robots have been adopted into society to help with menial tasks like cleaning or sorting products. The issue is primarily the accuracy of the robot’s control, as the sensors may provide an incorrect reading or latency in the actuator. A PID controller can be implemented to increase the robustness of the control by having three tunable parame...

This paper aims to comprehensively portray the sequence operations’ applicability: a mathematical tool for solving complex power system problems. Sequence operations only apply to discrete sequences, so understanding the steps of discretizing probability distributions to obtain corresponding discrete sequences is essential and is elucidated in this...

As the demand for renewable energy rises, optimizing wind power as an energy source is crucial. Wind power is one of the cleanest forms of energy available, and understanding the energy that wind turbines generate over time is necessary for building a better foundation for wind energy reliance. Previous research has explored using Long Short-Term M...

Unmanned aerial vehicles equipped with quadrotors have been used since the mid-twentieth century for various tasks, including transportation and delivery. These lightweight drones, made of materials such as carbon fiber or aluminum alloy, face challenges in energy consumption management due to problems with image processing, data processing, PID co...

The unpredictable nature of solar energy presents a significant obstacle to its effective incorporation into current grid systems. Global Horizontal Irradiance (GHI) is a critical factor in solar energy technology, as it directly influences the effectiveness of photovoltaic systems and solar thermal plants. Precise GHI forecasts are essential for t...

Cardiovascular disease ranks among the top causes of mortality, frequently caused by sudden obstructions within blood vessels. Timely identification and intervention are essential for minimizing the impact of the disease. This research employs image augmentation techniques to correct class imbalance in an ECG image dataset divided into five categor...

This paper proposes a modified ultra‐high gain DC–DC boost converter. The proposed converter has improved the performance of the conventional converter by reducing the total number of components (1 Capacitor and 1 Inductor) in the design structure. In addition, the continuous input current and high voltage gain features are retained in the proposed...

Quadcopter drones have become increasingly popular because of their versatility and usefulness in various applications, such as surveillance, delivery, and search and rescue operations. Weather conditions and obstacles can undoubtedly pose challenges for drone flights, sometimes causing the loss of one or two propellers. This is a significant chall...

The multivariable process plays a significant role in industrial applications, and designing a controller for the Multi-Input Multi-Output process is challenging due to dynamic process changes and interactions between system variables. Traditionally, the PI family of controllers has been used for its simple design, easy tuning, and quick deployment...

The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (H2S) and carbon dioxide (CO2), from natural gas before delivery to end-users. Consequently, chemical absorption-based acid gas removal units (AGRUs) are widely implemented due to their high efficiency and reliability. The most common solvent used in A...

This chapter investigates the fractal dimension of linear fractal interpolation with various settings. Additionally, the Riemann–Liouville fractional integral of a linear fractal interpolation function with variable scaling factors is studied. Also, the fractal approximation of the Rossler attractor based on different types of parameter and how the...

The utilization of renewable energy sources, par-ticularly wind power, has become increasingly significant in contemporary energy systems. This study focuses on applying data analytics and visualization techniques to forecast the gen-erated power of a wind turbine in Texas. The initial phase includes gathering data on parameters such as wind speed,...

This paper introduces an autonomous inspection system for solar panels and wind turbines utilizing Tello drones and the YOLOv8 object detection algorithm. The main objective is to establish an efficient method for identifying and evaluating these renewable energy components' conditions, focusing on detecting issues such as breakage and dust accumul...

Fractional calculus, a branch of mathematical analysis, extends traditional calculus that encompasses integrals and derivatives of non-integer orders [...]

The replication of human thinking by technology, including computer systems, is known as artificial intelligence (AI). The rise of AI can certainly help plant operations. In the past, process engineers manually predicted anomalous conditions in the plant to ensure every process was dependable and safe. This extensive procedure calls for an experien...

In real-world datasets, missed data is often expected due to sensor errors, environmental conditions, communication errors, and other technical limitations. These factors can affect the accuracy of power predictions, particularly in wind speed and direction parameters. Enhancing the accuracy of wind energy forecasts and maintaining the electrical g...

With the escalating demand for Radio Frequency Identification (RFID) technology and the Internet of Things (IoT), there is a growing need for sustainable and autonomous power solutions to energize low-powered devices. Consequently, there is a critical imperative to mitigate dependency on batteries during passive operation. This paper proposes the c...

This article examines the performance of the proposed complex-order, conventional and fractional-order controllers for process automation and control in process plants. The controllers are compared regarding disturbance rejection and set-point tracking, considering variables such as response time, robustness to uncertainty, and steady-state error....

Fractional-order systems and controls utilize concepts from fractional calculus for modelling, control designs, and practical applications. However, it can be challenging to transform these memory-dependent systems and controllers into hardware, which is why high-order integer systems are often used instead. Field Programmable Gate Arrays (FPGAs) a...

Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind’s complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind...

A quadrotor drone is an unmanned aerial vehicle used for transportation. Nowadays, drones have become increasingly popular in various fields. However, incorporating them into industrial applications has presented many challenges. One of the major obstacles has been providing communication between multiple drones. Several approaches have been propos...

Dry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, and cyber threats. To address these difficulties, proactive maintenance and management and a new technological strategy are needed to increase safety, reliability, and efficiency. A novel neural network model for...

This paper presents a technique of harmonic minimization from output voltage waveform of a reduced switch Multilevel Inverter (MLI) through an efficient bio inspired metaheuristic algorithm called Black widow optimization (BWO). The proposed reduced switch 13- level MLI scheme uses a single Photovoltaic (PV) source which can be suitable for grid in...

This article presents a novel complex fractional order (CFO) speed controller design and its implementation on an induction motor (IM) drive. This controller with multiple dimensions of control parameters as compared with existing industrial controllers provides more robust performance under variable operating conditions. Mostly, detuning of the ex...

Wireless technology is becoming increasingly critical in industrial environments in recent years, and the popular wireless standards are WirelessHART, ZigBee, WLAN and ISA100.11a, commonly used in closed-loop systems. However, wireless networks in closed-loop control experience packet loss or drops, system delay and data threats, leading to process...

Variability in the dataset of a variable refers to the periodically repeating component. Since variability comprises a specific pattern (skewed and/or multimodality) that is repeating; therefore, the pattern can be predictable. Modeling this predictable component has drawn enormous research interest in many engineering fields. When the variable of...

This paper focuses on the fractional-order Kalman filter’s growth, development, and application. Numerous advancement and the need for various application is critically investigated and summarized. The review work is done on fractional-order Kalman filters as they are best suited for constantly changing systems and can be used for estimating hidden...

Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these situations, but their performance is inadequate in systems and surroundings with varying set-points, lo...

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation...

The recent advancements in demand-side management techniques add significant benefits to the distribution systems. One such technique is transactive energy management systems (TEMS) which motivate the energy end-users to take part in local energy trading. The end-users can effectively increase the monetary benefits by trading the surplus generation...

Risk-based reliability assessment is prevalent for modern power systems under higher penetration of renewable generations. This paper highlights the importance of machine learning and probabilistic approaches for risk-based reliability assessment during power system operation and planning. A set of metrics for realistic risk-based reliability asses...

Robot manipulators are widely used in many fields and play a vital role in the assembly, maintenance, and servicing of future complex in-orbit infrastructures. They are also helpful in areas where it is undesirable for humans to go, for instance, during undersea exploration, in radioactive surroundings, and other hazardous places. Robotic manipulat...

The dynamic nature of energy harvesting rate, arising because of ever changing weather conditions, raises new concerns in energy harvesting based wireless sensor networks (EH-WSNs). Therefore, this drives the development of energy aware EH solutions. Formerly, many Medium Access Control (MAC) protocols have been developed for EH-WSNs. However, opti...

Special Issue "Applications of Fractional-Order Calculus in Robotics" A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Engineering". Deadline for manuscript submissions: 31 December 2022 https://www.mdpi.com/journal/fractalfract/special_issues/fractional_order_calculus_in_robotics

This book aims to provide a detailed understanding of applications of intelligent data-driven techniques for modelling, control, and optimization for various power and energy applications. Also, the book aims to develop multiple data-driven modelling for forecasting renewable energy sources. In all the cases, the benefits of intelligent data-driven...

Special session track titled "Advanced Data-Driven and Probabilistic Approaches for Modern Power Systems" in the 2nd International Symposium on Sustainable Energy and Technological Advancements which is hosted by NIT Meghalaya, India during 24th - 25th Feb. 2023. The conference is conducted in hybrid mode. We seek quality research papers from acade...

In the initial parts of this chapter, a detailed review of the PI controllers and their different modifications like predictive PI (PPI), fractional-order PI (FOPI), set-point weighted PI (SWPI), fuzzy PI (FPI), filtered predictive PI (FPPI), non-linear PI (NPI), iterative learning controller PI (ILCPI), internal model controller PI (IMCPI), and mo...

This chapter focuses on obtaining an optimal controller parameter with faster convergence and the best solution. Hence, to obtain the effective parameters, a novel arithmetic-trigonometric optimization algorithm is developed by using the essential arithmetic operators and the basic trigonometric functions. Different combinations of the proposed tec...

This chapter presents the design of fractional-order set-point and noise filters using the essential plant dynamics to handle the set-point load variations and stochastic noises. The proposed technique is compared with various existing methods using simulation analysis on benchmark process models and the real-time experimental investigation of the...

In the initial parts of this chapter, a detailed review of the PI-type iterative learning controllers with their different modifications is discussed. The second part discusses the development of a hybrid iterative learning controller-based fractional-order predictive PI controller (ILC-FOPPI) is presented with their learning function and q-filter...

Time series stationarity is vital for the effective implementation of forecasting models. Time series of renewable generation rich power system input variables such as photovoltaic generations, wind power generations, load power, and ambient temperature have inherent time series facets such as trend, seasonality, and volatility. These inherent face...

The book investigates the fractional calculus-based approaches and their benefits to adopting in complex real-time areas. Another objective is to provide initial solutions for new areas where fractional theory has yet to verify the expertise. The book focuses on the latest scientific interest and illustrates the basic idea of general fractional cal...

This paper focuses on developing a smart grid stability prediction model to handle the missing input variables. This paper implements two models. The first model is a feedforward neural network designed to predict stability with complete input data. The second model includes the novel work carried out, wherein two missing input variables are consid...

In this paper, the Riemann–Liouville fractional integral of an A-fractal function is explored by taking its vertical scaling factors in the block matrix as continuous functions from 0,1 to ℝ . As the scaling factors play a significant role in the generation of fractal functions, the necessary condition for the scaling factors in the block matrix is...

This paper focuses on the growth, development, and future of various forms of fractional-order neural networks. Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized. This also includes the recent trends in the dynamics of various fractional-order neural networks. The multiple forms of fra...

Chaos plays a prominent role in nonlinear systems like energy, finance, and weather. In these systems, a changing parameter to time yields a chaotic time series that contains a lot of system information. This information helps to analyze and understand the behavior of the chaotic system. However, traditional methods of investigating these chaotic t...

This paper focuses on deriving and validating the fractional-order form of rectified linear unit activation function and its linear and nonlinear variants. The linear variants include the leaky and parametric, whereas the nonlinear variants include the exponential, sigmoid-weighted, and Gaussian error functions. Besides, a standard formula has been...

The use of expansive pipeline networks guarantees domestic and industrial users for accessing a continuous flow of valuable liquids and gases. These pipeline systems were considered the most economical and safest pipeline of transport for oil and gas and are of great strategic importance. The risks during operating conditions need to be controlled...

In recent days, analysis of renewable-rich power systems has shown greater interest as the integration of renewable generations is encouraged nationwide both at transmission and distribution levels. The increasing integration of such sources poses many technical challenges that must be considered when designing, planning, and operating modern power...

A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can of...

Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review...

Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and being costly. Hence, we designed an automated model based on an artificial neural network to predict Say...

Bingi, KishoreB Rajanarayan PrustyPanda, Kaibalya PrasadPanda, GayadharThis paper concentrates on developing a forecasting model to reconstruct states in a chaotic fractional-order Rössler system. The proposed model’s attractiveness is how relationships between inputs (state variables) and outputs (change in state variables) are modeled for accurat...

Time series decomposition is extensively used recently for nonlinear non-stationary time series modeling and forecasting. A relevant set of monocomponents obtained using an adaptive decomposition method is a potential candidate for predictions using point and probabilistic forecasting frameworks. Time series decomposition has been widely applied to...

Arun Mozhi Devan, P.Hussin, Fawnizu AzmadiIbrahim, RosdiazliBingi, KishoreB Rajanarayan PrustyIn process control, signal filtering is a required field that needs continuous improvement for effective noise removal from the actual process signal. Processes implemented in noisy and uncertain environments are severely affected by external disturbance a...