In today's electricity system, power quality is a significant concern regardless of the voltage or current level. To address this issue, custom power devices (CPDs) have been introduced. In the considered system, six nonlinear load have been placed at different locations to introduce harmonics into the system. This paper focuses on strategic placement of CPDs in IEEE 15 bus system using MATLAB software. The primary objective is to minimize both the voltage total harmonic distortion (THD) and the individual harmonic distortion. The constraints ensure that the levels remain within the established standards, while also maintaining the voltage of each node within specified limits. Additionally, the study examines the performance of allocated CPD in controlling voltage quality and reactive power. In this work, particle swarm optimization (PSO) and adaptive particle swarm optimization (APSO) techniques have been employed to determine the optimal sizing and siting of CPDs. A comparison of the results obtained from these optimization methods is presented.
It is our immense pleasure to welcome you to the 5th International Conference on Energy, Power, and Environment (ICEPE 2023), that is being organized by Department of Electrical Engineering, National Institute of Technology Meghalaya, on 15th to 17th June 2023. ICEPE 2023 is technically Co-Sponsored by IEEE Smart Cities, IEEE Industry Applications Society (IAS), IEEE Kolkata, IEEE Guwahati Sub-Section, and others. Apart from this, 20% presented papers will be recommended to submit the extended version for publication in IEEE IAS Transactions”.
A novel Adaptive Fuzzy Campus Placement Optimization Algorithm (AFCPOA) is developed for solving unconstrained optimization problems. The proposed optimization algorithm is based on the concept of campus placement procedure adopted for offering a job to a student by an employer visiting campus for hiring students seeking employment. Fuzzy models are considered to depict written test and interview process. The performance of the proposed algorithm was tested on 10 benchmark optimization test functions and compared with other existing algorithms. Subsequently, the proposed algorithm is applied on IEEE 33 bus radial distribution system for optimal placement and sizing of Distributed Generators (DGs) to mitigate active power losses and voltage deviation. It is observed from the results that the proposed algorithm is more effective in comparison with existing algorithms.
Forecasting solar energy is essential for efficient grid management and integrating renewable energy sources into the electrical system. With the use of an extreme learning machine (ELM) and an adaptive moving average filter, we suggest a unique method for predicting solar power. The solar power time series' dynamic properties are captured by the adaptive moving average filter, which is used to preprocess the data. The solar power production prediction is then made using the ELM, a quick and effective machine learning technique. The preprocessed data is used to train the ELM, which then uses this knowledge to discover the nonlinear correlations between the input characteristics and solar power production.
In this paper, a rectenna (antenna + rectifier) is proposed for ambient RF energy harvesting. A modified rectangular microstrip patch antenna with inset feed is designed at 3.6 GHz with a gain of 4.3 dB. The antenna is compact in size with a layout area of $0.36\lambda \mathrm{x}\ 0.36\lambda$ where $\lambda$ is free space wavelength corresponding to resonating frequency. The antenna resonates at 3.6 GHz with impedance bandwidth (IBW) $(\mathbf{lS}_{11} < -20\ \mathbf{dB})$ of 4.53% and a radiation efficiency of 68.5% is noted. To hoard the available RF energy, a simple RF-DC rectifier is designed using equal ripple Low Pass Filter (LPF) on FR4 substrate with height of 1.6mm. To improve the efficiency of the rectifier and to achieve broad range power conversion efficiency (PCE), additional radial stubs are incorporated on the rectifier design. Maximum PCE of 60.7 % is achieved with a load of $2\mathbf{K}\Omega$ at 7dBm. The novelty of RF- DC rectifier is PCE of above 30% is achieved for broad range of input power (−10 dBm to 10 dBm).
Over these years, there has been an observable effect on the environment due to Global Climatic Change. Scientists all over the world have predicted many of these disasters, and the main reason behind this is the emission of greenhouse gas. The use of fossil fuels to generate electricity contributes significantly to greenhouse gas emissions. If this conventional fossil fuel-based energy is replaced by renewable energy resources like solar energy, wind energy, etc., more than half of the emissions can be curbed. Increasing the usage of renewable sources of energy, mostly photovoltaic devices, and enabling distributed energy services will thereby encourage an energy trading environment among the users. This paper focuses on providing an approach to implement a prototype that integrates a microgrid and blockchain to trade electricity without an intermediary. It comprises of smart contracts and an interface which together forms a microgrid management system creating a peer-to-peer platform for users to trade electricity and thereby contributing towards sustainable and affordable energy.
A perfect controller for DC-DC boost converter should be able to track the reference set point voltage that is higher than the source voltage along with minimum computational complexity. In this paper, a model predictive controller (MPC) is proposed for DC-DC boost converter control to regulate the output voltage under different operating conditions such as variation in input voltage and load current. The prediction is performed by the state space model and optimization is done using quadratic programming. The control performance of the converter is studied by varying prediction horizons with model disturbances. Finally, the hardware implementation of the proposed system is developed using dSPACE 1103 controller.
The present work proposes a wireless sensor network for underground coal mine environment monitoring using LoRa communication technology. The system consists of several inexpensive IoT sensors to assess environmental factors including temperature, humidity, air quality, and gas concentration. The designed monitoring unit is connected to the LoRaWAN network so that the units can interact with one another and also with the central server placed in the control room located above the ground. The work also presents a technique to extend the range of LoRa nodes so as to increase the coverage of LoRa network by relaying messages between devices and gateways inside the mine. Additionally a method based on spreading factor (SF) optimization is utilised for range extension. This work adjusts the SF of LoRa transmissions based on the distance between the devices. The range extension technique, which is integrated into the firmware of the devices, greatly extends the range and improves the dependability of LoRa communications in underground mines.
Transportation system is considered as the second largest source of carbon emissions after power generation. EVs (Electric Vehicles) play major role in reducing carbon emissions. Integration of EVs into the grid not only reduces carbon emissions but also protects environment. However there exists, many challenges during this integration. The major challenges faced during integration are overloading of power grid, power quality (PQ) issues, supply demand imbalance, increased peak demand, frequency deviation, energy losses, reduced stability and reliability, battery degradation and high investment cost. In this regard, few concerned issues from the above are considered and they are being mitigated by using RES as the main power supply. When these sources are unable to provide sufficient power to charge the vehicles, we rely on the energy stored in BESS for vehicle charging. In this event that BESS is also incapable of meeting the power demand, the Grid serves as an alternative power source for vehicle charging. This mitigation technique is being discussed and demonstrated using the MATLAB Simulink platform.
The most crucial factors in wind farm development are the best turbine selection, data collection accuracy, wind turbine positioning, and wake effect considerations. This work deals with the wind resource assessment (WRA) of an onshore wind farm with MERRA-2 Reanalysis data for repowering. The analysis is carried out by a comparison of Wind Resource Assessment (WRA) conducted through MERRA-2 Reanalysis data and meteorological data for investigating the variance through the comparison of MERRA- 2 and meteorological data, though the former is cheaper. The simulation is done using Wind Atlas Analysis and Application Program (WA $s$ P) software by Denmark Technological University (DTU), and the major results obtained from WA $s$ P after the wind farm modelling are Annual Energy Production (AEP), Wind Power Density (WPD), and Resource Grid.
Microgrid deployment has increased significantly in recent years due to the advantages of supplying load locally with increased reliability. However, the intermittent nature of renewables and presence of bi-directional power, and various modes of operation constitute different set of protection problems. This article presents a novel approach to microgrid protection using empirical mode decomposition (EMD) signal processing and minimum redundancy maximum relevance (MRMR) feature ranking coupled with k-nearest neighbor (KNN) classification. The proposed methodology leverages EMD to decompose the voltage and current signals into intrinsic mode functions (IMFs), which are then ranked using MRMR algorithm to select the most relevant features. KNN is then employed for fault detection and classification. The proposed method has been tested on a modified IEEE 13-bus distribution microgrid and performance indices validate the effectiveness of the proposed methodology in detecting and classifying faults.
Demand and response management plays a vital role in the smart grid for reliability between utility providers and smart meter customers. The demand of customers is predicted by load forecasting algorithms. However, existing load forecasting techniques consume high computation costs and limited forecasting accuracy. In this paper, Long Short-Term Memory (LSTM) is implemented for smart meter prediction of load. The proposed work is simulated on Open Energy Data Initiative (OEDI) dataset and our model outperforms the existing works in performance analysis. Mean Squared Error (MSE) and Mean Absolute Error (MAE) are achieved up to 0.0021 and 0.0332, respectively, by proposed model.
An efficient and reliable relay coordination scheme is essential to protect the power system from faults and damages due to unexpected events such as short circuits. To cope with the reverse direction of fault current dual-setting relays are evolved. This paper presents a novel protection coordination scheme based on a dual-setting rate-of-change-of-voltage (DS-ROCOV) relay which remains unaffected during load and generation changes and is applicable to different network topologies. The DS-ROCOV relay offers two different settings each for primary and backup protection. It also ensures that the protection system is sensitive to high resistance faults. The performance of the proposed protection coordination scheme is evaluated by simulating various fault conditions in IEEE 9-bus system using MATLAB/Simulink. The simulation results demonstrate that the proposed protection coordination scheme is effective in detecting and isolating faults while ensuring a quick response and minimizing unnecessary system outages. Overall, the proposed protection coordination scheme using DS-ROCOV enhances the reliability and selectivity of the protection systems in power distribution systems.
The changing amplitude and direction of short circuit current in the islanded mode (IM) and grid-connected mode (GM) make microgrid protection a significant problem. As relay coordination depends upon the fault current level, therefore, two different relay settings are required for proper coordination among relay pairs, one for each operating mode. Further, the performance of the coordination scheme depends upon the types of relays. In this context, this paper proposes a modified protection coordination scheme for microgrids by considering the user-defined dual setting directional overcurrent relays (DS-DOCRs) capable of providing coordination in both IM and GM. The main objective of the study is to determine the common optimal relay settings i.e., time multiplier setting (TMS), plug setting (PS) and relay characteristics constants (α) that can provide satisfactory coordination in each operating mode. The proposed approach is tested on a 7-bus microgrid (low voltage part of IEEE-14 bus system) and genetic algorithm (GA) is used as an optimization tool to determine the optimal relay settings.
Federated learning is a machine learning technique that allows multiple devices to collaboratively train a machine learning model without sharing their data with a central server. The data is kept on the local device, and the model is trained on the device itself. It also reduces the risk of data breaches and enhances privacy by keeping the data local to the device. It can improve the speed and efficiency of training large-scale models by distributing the workload across multiple devices. This paper proposes a solution to the security challenges faced by the federated learning framework, which allows global model construction without sharing raw data. Privacy-preserving and verifiable decentralized federated learning (PPVD-FL) is a framework designed for secure deep learning model training. It's a decentralized federated learning framework that pre-serves privacy and ensures verification. The framework uses an efficient and verifiable cipher-based matrix multiplication algorithm along with a suite of decentralized algorithms. It maintains the confidentiality of the global model and local updates and verifies every training step. PPVD-FL can protect privacy against various inference attacks and ensure training integrity, as shown by security analysis. Real-world experiments on datasets demonstrate its accuracy and practical performance.
Energy harvesting using Wiegand sensor as self-sustaining sensor is an emerging research area. The Wiegand sensor is a magnetic sensor that relies on the phenomenon of large Barkhausen jumps, generates a triangular voltage pulse upon the application of an alternating magnetic field. Pulse generation occurs regardless of how slowly the magnetic field variation occurs, is an attractive feature which enables its use as an energy harvester even in presence of low-frequency sources. Wiegand sensor as a sensing element have been used in many applications. But Wiegand sensor as energy harvester opens interesting, yet still overlooked possibilities in the energy harvesting field. The major challenge with the Wiegand sensor is energy harvested per pulse in the range of nJ. In this research work, we have proposed the circuit using Wiegand sensor that uses buck converter in discontinuous mode (DCM) mode to generate the digital pulse of 4.5 V and 10 ms at extremely low frequency for each magnetic field reversal without the use of any external power supply. Battery-less digital pulse generation is demonstrated for rotating application in the present study. The operating frequency up to 100 Hz has been considered. The main advantage of the proposed circuit is digitization of triangular shape Wiegand pulse is feasible for extremely slow varying magnetic field which can be used directly by microcontroller unit for further analysis.
The main aim of this study is to make an IOT-based home automation system utilizing a Wi-Fi based microcontroller. It is quite beneficial in leading a comfortable and high-quality lifestyle. A smartphone with an Android app for managing electrical household appliances. We used Node MCU(Esp8266), Wi-Fi module, Relay, IoT, smartphone, and other electrical components in this study. The benefits of this project include the ability to handle all our household appliances from a single location, which saves time and provides comfort. It is extremely advantageous to physically challenged and disabled individuals. It improves energy efficiency and is ideal for energy conservation. It is fast, dependable, and simple to use.
In the Smart Grid, communication lines and physical open access points are always prone to cyber-attacks, and electric theft is the most common one. To detect electricity theft, researchers have developed several advanced machine learning models. However, existing work has not explored the problem of data imbalance properly, which is one of the significant challenges in electricity consumption data. This paper aims to compare various data balancing techniques and present an integrated theft detection model. This paper presents a multi-layer model for detecting fraudulent consumers in the smart grid. The detection process starts with data preparation steps, which include data interpolation, outlier handling, and data standardization. The next crucial step is handling data imbalance. Various techniques are tested, and AdaSys performs better than others. The model is being trained on a balanced dataset and validated on a real imbalanced dataset for realistic results. For higher performance, a two-layer model is chosen for electricity theft detection. The first layer consists of three heterogeneous machine learning models, and an Artificial Neural Network (ANN) model is used for the second layer. The first layer's probabilistic prediction serves as input to the second layer, which makes the final prediction. Experimental results confirm that multilayer model classifiers perform better than individual classifiers for detecting cyber-attacks on real consumption datasets.
When an external magnetic field is applied to a ferromagnetic material like Galfenol, Terfenol-D, etc., they generally tend to expand or contract. This property of the materials is known as magnetostriction. Magnetostrictive materials thereby convert the applied electromagnetic energy into harness able mechanical energy and can be utilized in various applications. The Villari effect or the inverse magnetostrictive effect is just the opposite of Joule's effect of magnetostriction. It refers to the phenomenon in which the application of external stress causes a change in the magnetic domains of the materials and thus an electromotive force is generated in the material. In other words, in inverse magnetostriction, the mechanical energy thus gets converted to electromagnetic energy. This electromagnetic energy or rather the voltage obtained is put to various other applications such as a source of self-sustained power supply of wireless sensor networks especially in harsh and hostile environments. Also, it can be said that this effect of inverse magnetostriction is related to ambient vibrations and thus can also to put to various practical uses such as the health monitoring of civil structures. But for putting the magnetostrictive energy sensors in that use, it's important to decide which of the parameters between voltage and frequency can be the deciding factor. The paper tries to find out the same by the application of various hardware equipment and understanding the relation.
Solar cookers use the sun's radiation energy to cook food, providing an alternative to traditional cooking methods. Box-type solar cookers are simple to construct and operate, making them a popular choice for use in rural areas and developing countries. However, the performance of a box-type solar cooker may vary depending on the prevailing weather conditions, which may affect the amount of solar energy received and the cooking temperature achieved. This research paper aims to investigate the effect of weather conditions on the performance of a Modified solar cooker with inclined outer cover (MSCIOC). The study involved conducting experiments under different weather conditions, including sunny, partly cloudy, and overcast days, to determine the impact of these conditions on the cooking temperature achieved and the cooking time. The figure of merits obtained ( $\mathbf{F}_{\mathbf{1}}$ & $\mathbf{F}_{\mathbf{2}}$ ) are 0.113 and 0.41, cooking power is 86 W, cooking time is 252 minute and theoretical cooking time is 233 minutes. The results of this study will provide insights into the optimal conditions for using a box-type solar cooker and inform the development of strategies for improving their performance.
