This paper proposes a novel improved polar bear optimization (IPBO) algorithm and employs it along with polar bear optimization (PBO) and chaotic population-based variants of polar bear optimization algorithm to solve combined economic emission dispatch (CEED) problem. PBO is a meta-heuristic technique inspired by the hunting mechanisms of polar bears in harsh arctic regions based only on their sense of sight. Polar bears in nature exhibits hunting of prey not only on their sight but also on their keen sense of smell. Hence, a novel improved variant of PBO which enhances its operation by equipping it with tracking capabilities utilizing polar bears sense of smell has been proposed in this study. The validity of novel IPBO is tested through 5 benchmark functions and 140 units Korean ED problem. Furthermore, the impact of different population initialization methods is also observed on the capabilities of conventional PBO. The proposed chaotic population based PBO, improved PBO (IPBO) and PBO are employed to solve IEEE 3 unit and 6-unit CEED problem. CEED is a multi-objective power system optimization problem with conflicting objectives of cost and emission. The simulations performed undertake each objective individually as well as collectively. The results achieved by each technique are analyzed statistically through Wilcoxon rank sum test (WRST), probability density function and cumulative density function. Both the statistical and numerical analysis of results showcase the strength of each solution technique as well as their ability to improve cost and emissions in the solution of CEED problem.

This article presents multiple strategies to handle ramp rate in dynamic economic load dispatch (DELD) problem. DELD is a power system optimization problem that tackles the dispatch of thermal generators over a period of 24 h under numerous constraints like power balance, ramp rate, rippling effect and generator limits. All these constraints make DELD highly non-linear, complex, multi-dimensional and computationally intensive. In our research, we have devised unique ramp rate handling strategies and employed grey wolf optimizer (GWO) to implement them in MATLAB environment for the solution of IEEE standard 5-unit DELD system. GWO is a meta-heuristic optimization algorithm that has extensive global exploration and strong local exploitation capabilities. Addition of ramp rate handling strategies to the solution of DELD by GWO yielded diverse results. Each of these was statistically analyzed and the best results were compared with other techniques cited in the literature. Comparison indicated improvement in cost and solution time.

This paper presents a novel Arduino-based fault detection and protection system for power transformers. Power transformers are an integral component of the power system infrastructure. Power transformers are present in such a significant number in the power architecture that any alteration in its operation effects the whole power system. The optimal operation of the transformer depends upon its operating condition; for this reason, its monitoring and protection are very important. Currently, power transformers employ differential relays to ensure optimal operation, but differential relays are unable to ascertain conditions such as overloading and intra turn faults. In this paper, Arduino was used to monitor transformer operation instead of differential relays and generate tripping or alert signals based on sensed values. Arduino autonomously sensed the current, voltage, and temperature values of the power transformer round the clock and handled any fault by comparing preset values of these parameters. In addition, the differential relay functionality of fault detection was implemented in the Arduino environment. Whenever a fault occurred, Arduino sent the fault signal to a Wi-Fi module, which was then displayed in the Blynk app. The practical implementation of this proposed system was tested, and its operation was found to be effective in fault detection.

This paper presents the solution of highly complex, non-linear, multi-objective Dynamic Combined Economic Emission Dispatch (DCEED) problem. DCEED is a power system optimization problem with conflicting objectives of fuel cost and emission. DCEED includes constraints like valve point loading effect, Transmission Losses and Ramp Rate limits. Solution of DCEED problem is given by a novel Hybridized Flower Pollination Algorithm (FPA) with Sequential Quadratic Programming (SQP). FPA is a nature inspired population based meta-heuristic optimization technique that models its search on the flower pollination process. The non-convex nature of generation because of numerous operational, physical and dynamic constraints, makes search space highly multi model and complex. This makes DCEED a challenging as well as an attractive problem for research. The effectiveness of FPA-SQP is tested and validated by applying it on IEEE Standard 5-unit and 10-unit non-convex test system in MATLAB environment for the time interval of 24 hours. The results achieved by this algorithm show significant reduction in cost and emission as compared to other available techniques in the literature.

Artificially intelligent advances such as tech gloves allow handicapped wearers to handle daily matters as normal. A wearable hand-rehabilitation system, i.e., a robotic arm, is engineered with controlled programming to control a disabled hand with features such as movement of fingers and holding items. A life-threatening disease (stroke) is caused when brain cells start to die, causing around 50–70% of patients to face paralysis and disability. People may face after-effects such as reduced use of the hand and limb or a paralyzed hand. Many methods have been introduced to overcome these issues, including therapies, but they are not so reliable when overcoming disability issues. To overcome these issues, we proposed a smart robotic hand that encounters hand disability issues. The smart robotic hand will aid the hands of disabled people by replacing their disabled hand with the smart robotic hand and by controlling the movement of the robot with the movement of the other hand. This can also be helpful for environments where it is not feasible for humans to work, such as in nuclear reactors and in bomb disposal squads. Some people have disabilities of the hand, so this smart robotic hand can also be used in that scenario. The robotic hand is mainly controlled through a flex sensor. By using Arduino, flex sensor outputs are mapped accordingly to the servo motors. The robot is controlled by a wired arrangement.