Akash Kumar

Due to the ongoing sustainability drive, nanogrids are getting attention. Despite its perceived advantages, NG's high load volatility poses a risk to the stability of the connected power network. This is exacerbated by the user's various energy usage behaviors and the uncertain nature of meteorological events, leading to difficulties in load prediction. If the NG shiftable load can be well predicted, the user may move it from peak to off-peak hours to reduce energy costs, but at the expense of… Show more

Due to the ongoing sustainability drive, nanogrids are getting attention. Despite its perceived advantages, NG's high load volatility poses a risk to the stability of the connected power network. This is exacerbated by the user's various energy usage behaviors and the uncertain nature of meteorological events, leading to difficulties in load prediction. If the NG shiftable load can be well predicted, the user may move it from peak to off-peak hours to reduce energy costs, but at the expense of comfort. Existing demand-side management models are predominantly focused on MGs with lesser volatile loads and available shiftable loads, and comfort is not well studied in these models. In addition, most NGs have either limited or no data about their shiftable loads’ operations. To address these challenges, a comprehensive predictive demand-side management (PDSM) approach with two components is developed in this paper. The 1st component is to predict the day-ahead shiftable load where the Stacked-Long Short-Term Memory (SLSTM), ANN, and Shiftable Equipment Matrix (SEM) modules are integrated. The SLSTM module predicts day-ahead load variations (%) using load time series data which is segregated by the percentile-based method. The ANN module with dynamic feature selection predicts day-ahead load using k-means based on historical meteorological and load data. The SEM module derives the average percentage shiftable equipment load using electric data from neighboring NGs. The second component is user-centric multi-objective optimization through load shifting. A user-centric Mixed Integer Quadratic Programming optimization model is developed to shift the predicted shiftable load to minimize the energy cost and discomfort for the user. Results show that the SLSTM predicts variations with R2 of 97.6%, MAPE of 9.7%, and MSE of 0.027% and the integrated approach predicts shiftable load with R2 of 95.84%. In addition, daily energy costs can be saved up to 5.17% through e optimization. Show less

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations… Show more

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours”, raises the energy cost substantially. In this paper, an ANN model with dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h (t-1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square Error (MSE) of 0.03 KW, the Mean Absolute Percentage Error (MAPE) of 9%, and the coefficient of variation (CV) of 11.9% and results in an average of 20% daily energy cost savings by shifting peak load to off-peak hours. Show less

Micro-grids’ operations offer local reliability; in the event of faults or low voltage/frequency events on the utility side, micro-grids can disconnect from the main grid and operate autonomously while providing a continued supply of power to local customers. With the ever-increasing penetration of renewable generation, however, operations of micro-grids become increasingly complicated because of the associated fluctuations of voltages. As a result, transformer taps are adjusted frequently… Show more

Micro-grids’ operations offer local reliability; in the event of faults or low voltage/frequency events on the utility side, micro-grids can disconnect from the main grid and operate autonomously while providing a continued supply of power to local customers. With the ever-increasing penetration of renewable generation, however, operations of micro-grids become increasingly complicated because of the associated fluctuations of voltages. As a result, transformer taps are adjusted frequently, thereby leading to fast degradation of expensive tap-changer transformers. In the islanding mode, the difficulties also come from the drop in voltage and frequency upon disconnecting from the main grid. To appropriately model the above, non-linear AC power flow constraints are necessary. Computationally, the discrete nature of tap-changer operations and the stochasticity caused by renewables add two layers of difficulty on top of a complicated AC-OPF problem. To resolve the above computational difficulties, the main principles of the recently developed “l1-proximal” Surrogate Lagrangian Relaxation are extended. Testing results based on the nine-bus system demonstrate the efficiency of the method to obtain the exact feasible solutions for micro-grid operations, thereby avoiding approximations inherent to existing methods; in particular, fast convergence of the method to feasible solutions is demonstrated. It is also demonstrated that through the optimization, the number of tap changes is drastically reduced, and the method is capable of efficiently handling networks with meshed topologies Show less

Surface cracks can be the bellwether of the failure of a road. Hence, crack detection is indispensable for the condition monitoring and quality control of road surfaces. Pavement images have high levels of intensity variation and texture content; hence, the crack detection is generally difficult. Moreover, shallow cracks are very low contrast, making their detection difficult. Therefore, studies on pavement crack detection are active even after years of research. The fuzzy Hough transform is… Show more

Surface cracks can be the bellwether of the failure of a road. Hence, crack detection is indispensable for the condition monitoring and quality control of road surfaces. Pavement images have high levels of intensity variation and texture content; hence, the crack detection is generally difficult. Moreover, shallow cracks are very low contrast, making their detection difficult. Therefore, studies on pavement crack detection are active even after years of research. The fuzzy Hough transform is employed, for the first time, to detect cracks from pavement images. A careful consideration is given to the fact that cracks consist of near straight segments embedded in a surface of considerable texture. In this regard, the fuzzy part of the algorithm tackles the segments that are not perfectly straight. Moreover, tiled detection helps reduce the contribution of texture and noise pixels to the accumulator array. The proposed algorithm is compared against a state-of-the-art algorithm for a number of crack datasets, demonstrating its strengths. Precision and recall values of more than 75% are obtained, on different image sets of varying textures and other effects, captured by industrial pavement imagers. The paper also recommends numerical values for parameters used in the proposed method. Show less

Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture nonuniformities that make their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough, and expedited health monitoring of roads. In the pavement monitoring area, well-known texture descriptors, such as gray-level co-occurrence matrices and local binary patterns, are often used… Show more

Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture nonuniformities that make their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough, and expedited health monitoring of roads. In the pavement monitoring area, well-known texture descriptors, such as gray-level co-occurrence matrices and local binary patterns, are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB® and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV-based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature. Show less

Aerial Robots are in major focus nowadays for security and surveillance purposes. The paper presents a vision-based novel approach using an on-ground marker using Laser diode to stabilize the orientation of quad-rotor under hovering condition for indoor environments. For this purpose, a Laser diode mounted at the inertial center of the quad-rotor is used to detect the disturbance rather than using a traditional Inertial Measurement Unit based approach. Ground images obtained from camera are… Show more

Aerial Robots are in major focus nowadays for security and surveillance purposes. The paper presents a vision-based novel approach using an on-ground marker using Laser diode to stabilize the orientation of quad-rotor under hovering condition for indoor environments. For this purpose, a Laser diode mounted at the inertial center of the quad-rotor is used to detect the disturbance rather than using a traditional Inertial Measurement Unit based approach. Ground images obtained from camera are binarized to locate the position of marker in order to estimate change in position. Affine transforms are then used to calculate roll, pitch and yaw, a Proportional-Derivative controller is then tuned using the calculated values to stabilize the aerial robot. The technique is simulated in Matlab Simulink environment. The technique shows promising results with average settling time of 0.9656 secs Show less