Optimization-based Techniques Research Articles

A study in enhancement of satellite images

Satellite image enhancement is the method, which is commonly used in the field of satellite image processing for the betterment of the feature visualization and for clear visibility to properly identify those features. These satellite images have many atmospheric barriers like distortion and noise because they are captured from a very high and far distance. After the image capturing, some geometric corrections and radiometric corrections are carried out on those images, but these corrections are not enough for all the applications. So, it is needed to enhance the image restored before using it for feature identification. Nowadays, there are so many approaches for assuring effective image enhancement. However, there are so many drawbacks that need to be addressed in future. Hence, this article illustrates a clear and elaborate survey of various research articles presenting satellite image enhancement methods, such as machine learning-based techniques, optimization-based techniques, wavelet-based techniques, filter-based techniques and Histogram equalization-based techniques. Finally, the analysis is given in the survey based on the classification of research methods, publication year, utilized datasets and performance measures towards enhancement of satellite images. At last, issues and research gaps in methods are given in such a way that the motivation for developing an effective method for enabling effective enhancement is revealed.

Efficient Reinforcement Learning for 3D Jumping Monopods.

We consider a complex control problem: making a monopod accurately reach a target with a single jump. The monopod can jump in any direction at different elevations of the terrain. This is a paradigm for a much larger class of problems, which are extremely challenging and computationally expensive to solve using standard optimization-based techniques. Reinforcement learning (RL) is an interesting alternative, but an end-to-end approach in which the controller must learn everything from scratch can be non-trivial with a sparse-reward task like jumping. Our solution is to guide the learning process within an RL framework leveraging nature-inspired heuristic knowledge. This expedient brings widespread benefits, such as a drastic reduction of learning time, and the ability to learn and compensate for possible errors in the low-level execution of the motion. Our simulation results reveal a clear advantage of our solution against both optimization-based and end-to-end RL approaches.

Prescriptive analytics for low and high water management in German waterways

Abstract The real-time management of multi-purpose storage reservoirs aims at an efficient operation of existing hydraulic infrastructure. This management process can be structured as a prescriptive analytics setup that considers both current and predicted system states to recommend actions and outline potential implications. In application to a reservoir and river system, it combines hydrological modelling components for the system schematization, observations and data assimilation for the identification of the current system state, meteorological and hydrological predictions as well as optimization-based techniques to support decision-making regarding reservoir operations. In this paper, we present the application of such a framework to the short-term management of the Eder and Diemel storage reservoirs. These reservoirs are operated by the German Federal Waterways and Shipping Administration (WSV) with the primary goal to support navigation in the River Weser during low flow periods. In addition, partially conflicting objectives such as flood protection, energy generation and recreation are considered. The implementation includes an explicit consideration of forecast uncertainty and its impact on the decision-making by using probabilistic forecasts in combination with a multi-stage stochastic optimization approach. We demonstrate the applicability of the approach based on low and high water use cases. Special attention is paid on the benefits of the probabilistic forecast in combination with the multi-stage stochastic optimization versus a deterministic setup. It provides an explicit translation of the forecast uncertainty in the decision variables, in this case the reservoir releases helping the operators to better anticipate the range of future release decisions. Furthermore, the stochastic approach is expected to provide more stable decisions in an operational setting, based on more stable forecasts by considering various possible realizations of the future instead of picking a single one, which gets random after 4–5 days.

On the simplification of the internal nonlinear robot models for the MPC-based visual servoing

Visual servoing enables tracking and grasping of static and moving objects and locating regions of interest. Its implementation requires the coordination of low-level control tasks with high-level supervision and planning. The use of the PID-based manipulation control addresses only the dynamical response and requires the use of optimization-based techniques at the supervisory level. On the contrary, model predictive control (MPC) simplifies the design as it combines both tasks within a single algorithm. However, successful MPC implementation depends on the quality of the internal model utilized by the algorithm. Robots are intrinsically and significantly nonlinear control objects, so their models are inherently complex. This leads to computational problems. Standard solutions are to shorten the MPC horizons or to parallelize calculations. The approach suggested here returns to the problem roots, i.e. to the model. This study proposes simplified models that allow the use of the MPC. General Hammerstein–Wiener-like model is applied to an arm, which is further simplified into the linear dynamics used as the predictive supervisory control over the torque control. The head uses linear dynamics used by the supervisory MPC working with the base servomotors. It is shown that they are computationally efficient and sufficiently accurate. It is shown that the linear MPC controllers, which use the suggested simplified dynamic robot models, can successfully support visual servoing with accurate control. They are compared with standard PID-based structures and show their superiority. The proposed approach is successfully validated using Matlab and Gazebo robot simulator and is ultimately confirmed by experiments on a real robot.

An efficient MPPT techniques for inter-harmonic reduction in grid connected hybrid wind and solar energy systems

In this work, the operation of photovoltaic system, wind turbine driven doubly fed induction generator along with battery has been observed. Also, a searching space minimization-based artificial bee colony scheme is developed for tracking the maximum power in a doubly fed induction generator-based system. To track maximum power in solar systems, an improved adaptive reference voltage approach has been presented. Several conventional and optimization-based techniques are used by DFIG and photovoltaic systems to get around the non-linearity features in the output parameters. Regarding DFIG, the artificial bee colony method based on searching space minimization can be used to solve the shortcomings of the perturb and observe algorithm. Because of its weather-sensitive nature, it can withstand sudden changes in wind speed. The suggested searching space minimization based artificial bee colony strategy uses a mechanism for determining the range of optimal rotor speed in order to track the maximum power point more quickly. The maximum power point tracking performance of the adaptive reference voltage technique is superior to that of current perturb and observed-based systems. However, a huge processing memory is required in order to track the maximum possible power point. This paper proposes an enhanced maximum power point tracking technique based on adaptive reference voltage that does not require a memory unit. Additionally, despite sudden changes in irradiation conditions, improved adaptive reference voltage can drift-free and reliably monitor the maximum power point. The new adaptive reference voltage technique uses temperature and radiation sensors to identify the region nearest to the maximum power point. This helps the system respond more quickly. The proposed system with searching space minimization based artificial bee colony and improved adaptive reference voltage schemes displays lower inter-harmonic content in grid current compared to perturb and observe scheme. The proposed scheme has been implemented in MATLAB & simulink atmosphere and OPAL-RT displayed satisfactory results.

In-depth Multi-Objective charge pump design space exploration towards the automatic synthesis of power management units

The inclusion of automation mechanisms in the intricate design of power management units (PMUs) is long pursued in industry. This paper addresses a mixed-signal design flow whose purpose is to automatically produce, from the system-level to the ready-for-tape-out layout, complex PMUs when given a set of specifications. Particularly, in that flow, at the IP level, the proper analysis of the design tradeoffs is tedious and impractical, as a large amount of conflicting performance figures obtained from transient analysis for typical and multiple process/temperature corners, must be considered simultaneously. Therefore, it is common for the design cycle of a single integrated charge pump required in a state-of-the-art PMU to surpass a full month, depending on the intended technology, devices’ types and operation voltages. Additionally, by the end of this process, circuit designers often end up with a limited vision of the attainable performances. However, due to the recent improvements in computational power of modern workstations, optimization-based techniques with expensive evaluation mechanisms are now perceived as a potential solution to pursue optimal sizing and layout. Therefore, an academic electronic design automation tool is adapted and applied to explore the performance boundaries of a charge pump in a 180-nm technology node and determine the optimal tradeoffs between performance and the, severely constrained, layout area. In the last step, a worst-case corner optimization on a 45- and 99- dimensional design and performance spaces, respectively, derived from 9 time-consuming transient analysis of each candidate solution, produced 35 design solutions that provide a thorough analysis between worst-case efficiency, voltage drop, and rise and fall times, impossible to perform in the manual design. The obtained insights on the design space were used to speed-up the process of devising a solution for tape-out.

Fractional-order model identification based on the process reaction curve: A unified framework for chemical processes

This study introduces a novel method for identifying dynamic systems aimed at deriving reduced-fractional-order models. Applicable to processes exhibiting an S-shaped step response, the method effectively characterizes fractional behavior within the range of fractional orders (α∈[0.5,1.0]). The uniqueness of this approach lies in its hybrid nature, combining one-variable optimization techniques for estimating the model fractional order α with analytical expressions to estimate parameters T and L. This hybrid approach leverages information from the reaction curve obtained through an open-loop step-test experiment. The proposed method demonstrates its efficacy and simplicity through several illustrative examples, showcasing its advantages over established analytical and optimization-based techniques. Notably, the hybrid approach proves particularly advantageous compared to methods relying on the process reaction curve. To highlight its practical applicability, the identification algorithm based on this hybrid approach is implemented on hardware using a microprocessor. The experimental prototype successfully identifies the First-Order Plus Dead Time (FFOPDT) model of a thermal-based process, validating the proposed method's real-world utility.

Fast nonlinear cross-sparse filtering for rolling bearings compound fault diagnosis

The investigation of faults in rotating machinery has been thoroughly examined. Among the different methods under exploration, sparse optimization-based techniques have arisen as a highly desirable approach. However, in real industrial environments, the collected bearing signals often contain a random impact component resulting from changes in working conditions and load mutations. When a machine malfunctions, it can readily induce and generate new faults, resulting in composite faults. To address this challenge, this paper proposes a novel multidimensional blind deconvolution method named fast nonlinear cross-sparse filtering (FNCr-SF). The FNCr-SF aims to separate weak compound faults under random impact interference. Various preprocessing techniques, including Z-score normalization and nonlinear sigmoid activation function, are employed to amplify the faint characteristics of compound faults and minimize the influence of random interference. Furthermore, the FNCr-SF method enables adaptive decomposition of fault components without the need for prior knowledge or pre-processing. This approach effectively reduces random interference and accurately detects compound faults in bearings. Experimental and simulation signals validate the effectiveness of the FNCr-SF method in compound fault detection, demonstrating its high accuracy and robustness.

Uncertainty parameters of battery energy storage integrated grid and their modeling approaches: A review and future research directions

The continuously growing population and urban growth rates are responsible for the sharp rise in energy consumption, which leads to increased CO2 emissions and demand-supply imbalances. The power sector is switching to alternative energy sources, including renewable energy resources (RES) such as Photovoltaic (PV) and wind power (WP) and battery energy storage systems (BESS), among others, due to an increase in the use of fossil fuels and their shortage. Since the power generation of these resources is uncertain due to climatic fluctuations and the direct integration of these resources into the power grid is very complex due to the issues such as; voltage and frequency regulation, overloading of active transmission lines, and supply-demand disparity, the research on system uncertainties is receiving increasing attention. This study provides a comprehensive analysis of the several parameters of uncertainty, approaches for dealing with the uncertainty in battery energy storage (BES)-based RES integrated grid, and the advantages and disadvantages of each method. Moreover, various analytical and numerical approaches were developed for integrating RES and BESS into the power grid, including probabilistic methods, possibilistic methods, robust optimization-based techniques, and machine learning algorithms. The comparative analysis of these approaches highlights their relative strengths and weaknesses, providing a valuable resource for researchers and utility planners. Additionally, this review paper identifies several issues and challenges associated with the integration of RES and BESS into the power grid, such as power quality, economical effect, battery aging effect, and environmental effect. Furthermore, the paper suggests a few future research directions, including the development of novel models for analyzing uncertainty in power systems, coordination of uncertainty parameters, integration of BESS into RES and grid, power electronics integration, and environmental factor. Overall, this article's novel contributions include a comprehensive analysis of uncertainty parameters, a comparative analysis of uncertainty modeling approaches, an identification of critical issues and challenges, and the suggestion of future research directions to promote a sustainable and reliable power system. This article will aid in defining the requirements and specifications for novel models for analyzing uncertainty in power systems. The discussion and analysis will assist researchers and utility planners in selecting a suitable uncertainty modeling approach with significant penetrations of distributed RESs, which can lead to achieving a reliable and sustainable power system.

GWO-Based Multi-Stage Algorithm for PMDC Motor Parameter Estimation.

During the design of a wheeled mobile robot, the problem of the proper selection of the parameters of its motor controllers was encountered. Knowing the parameters of the robot's Permanent Magnet Direct Current (PMDC) motors, precise tuning of the controllers can be performed, which then results in improved robot dynamics. Among many methods of parametric model identification, optimization-based techniques, particularly genetic algorithms, have gained more and more interest recently. The articles on this topic present the results of parameter identification, but they do not refer to the search ranges for individual parameters. With too wide a range, genetic algorithms do not find solutions or are time-inefficient. This article introduces a method for determining the parameters of a PMDC motor. The proposed method performs an initial estimation of the range of searched parameters to shorten the estimation time of the bioinspired optimization algorithm.

Convex Optimization-Based Techniques for Trajectory Design and Control of Nonlinear Systems with Polytopic Range

This paper presents new techniques for the trajectory design and control of nonlinear dynamical systems. The technique uses a convex polytope to bound the range of the nonlinear function and associates with each vertex an auxiliary linear system. Provided controls associated with the linear systems can be generated to satisfy an ordering constraint, the nonlinear control is computable by the interpolation of controls obtained by convex optimization. This theoretical result leads to two numerical approaches for solving the nonlinear constrained problem: one requires solving a single convex optimization problem and the other requires solving a sequence of convex optimization problems. The approaches are applied to two practical problems in aerospace engineering: a constrained relative orbital motion problem and an attitude control problem. The solve times for both problems and approaches are on the order of seconds. It is concluded that these techniques are rigorous and of practical use in solving nonlinear trajectory design and control problems.

Data clustering using leaders and followers optimization and differential evolution

Data clustering is an important research topic in data mining. Although cluster analysis based on optimization algorithms has attracted great attention, optimization-based techniques face difficulties due to the non-linear objective function and complicated search space. Leaders and followers optimization (LaF) introduced in the 2015 IEEE Congress on Evolutionary Computation, and differential evolution algorithm (DE) are two efficient evolutionary computation methods, and they own some special advantages. The key power of LaF is the exploration in multi-modal search spaces, but it has a poor performance in the exploitation. On the other hand, DE based on the DE/best/1 mutation operator significantly promotes the exploitation process. In this study, the strong properties of LaF and DE are combined to balance exploration and exploitation in the search space to discover the cluster centroids. Besides, the proposed clustering method, i.e., LaF-DE, does not need parameter settings, unlike the existing optimization-based partitional clustering methods. Hence, this study proposes a straightforward, parameter-free, and efficient novel hybrid algorithm for the optimization-based partitional data clustering problem. Many experiments on the functions from CEC2017 test suite show that LaF-DE has better optimization performance and higher stability than the state-of-the-art metaheuristic algorithms. LaF-DE has been compared with well-known clustering techniques on the UCI and Shape datasets. The experimental results and statistical tests indicate that LaF-DE outperforms the well-known partitional clustering methods on 8 of 12 datasets in terms of internal performance metrics. Besides, LaF-DE performs better than density peaks clustering on 9 of 12 datasets in terms of external performance metrics.

Cleaning ground truth data in software task assignment

In the context of collaborative software development, there are many application areas of task assignment such as assigning a developer to fix a bug, or assigning a code reviewer to a pull request. Most task assignment techniques in the literature build and evaluate their models based on datasets collected from real projects. The techniques invariably presume that these datasets reliably represent the “ground truth”. In a project dataset used to build an automated task assignment system, the recommended assignee for the task is usually assumed to be the best assignee for that task. However, in practice, the task assignee may not be the best possible task assignee, or even a sufficiently qualified one. We aim to clean up the ground truth by removing the samples that are potentially problematic or suspect with the assumption that removing such samples would reduce any systematic labeling bias in the dataset and lead to performance improvements. We devised a debiasing method to detect potentially problematic samples in task assignment datasets. We then evaluated the method’s impact on the performance of seven task assignment techniques by comparing the Mean Reciprocal Rank (MRR) scores before and after debiasing. We used two different task assignment applications for this purpose: Code Reviewer Recommendation (CRR) and Bug Assignment (BA). In the CRR application, we achieved an average MRR improvement of 18.17% for the three learning-based techniques tested on two datasets. No significant improvements were observed for the two optimization-based techniques tested on the same datasets. In the BA application, we achieved a similar average MRR improvement of 18.40% for the two learning-based techniques tested on four different datasets. Debiasing the ground truth data by removing suspect samples can help improve the performance of learning-based techniques in software task assignment applications. • Devised a debiasing method to clean task assignment datasets. • Conducted experiments in two task assignment applications. • Debiasing the ground truth data improves learning-based techniques’ performance.

Second-Order Ultrasound Elastography With L1-Norm Spatial Regularization.

Time delay estimation (TDE) between two radio-frequency (RF) frames is one of the major steps of quasi-static ultrasound elastography, which detects tissue pathology by estimating its mechanical properties. Regularized optimization-based techniques, a prominent class of TDE algorithms, optimize a nonlinear energy functional consisting of data constancy and spatial continuity constraints to obtain the displacement and strain maps between the time-series frames under consideration. The existing optimization-based TDE methods often consider the L2 -norm of displacement derivatives to construct the regularizer. However, such a formulation over-penalizes the displacement irregularity and poses two major issues to the estimated strain field. First, the boundaries between different tissues are blurred. Second, the visual contrast between the target and the background is suboptimal. To resolve these issues, herein, we propose a novel TDE algorithm where instead of L2 -, L1 -norms of both first- and second-order displacement derivatives are taken into account to devise the continuity functional. We handle the non-differentiability of L1 -norm by smoothing the absolute value function's sharp corner and optimize the resulting cost function in an iterative manner. We call our technique Second-Order Ultrasound eLastography (SOUL) with the L1 -norm spatial regularization ( L1 -SOUL). In terms of both sharpness and visual contrast, L1 -SOUL substantially outperforms GLobal Ultrasound Elastography (GLUE), tOtal Variation rEgulaRization and WINDow-based time delay estimation (OVERWIND), and SOUL, three recently published TDE algorithms in all validation experiments performed in this study. In cases of simulated, phantom, and in vivo datasets, respectively, L1 -SOUL achieves 67.8%, 46.81%, and 117.35% improvements of contrast-to-noise ratio (CNR) over SOUL. The L1 -SOUL code can be downloaded from http://code.sonography.ai.

Automated human cell classification in sparse datasets using few-shot learning

Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an active area of research involves automating cell classification through use of deep learning-based techniques. In practice, a large amount of data is required to accurately train these deep learning models. However, due to the sparse human cell datasets currently available, the performance of these models is typically low. This study investigates the feasibility of using few-shot learning-based techniques to mitigate the data requirements for accurate training. The study is comprised of three parts: First, current state-of-the-art few-shot learning techniques are evaluated on human cell classification. The selected techniques are trained on a non-medical dataset and then tested on two out-of-domain, human cell datasets. The results indicate that, overall, the test accuracy of state-of-the-art techniques decreased by at least 30% when transitioning from a non-medical dataset to a medical dataset. Reptile and EPNet were the top performing techniques tested on the BCCD dataset and HEp-2 dataset respectively. Second, this study evaluates the potential benefits, if any, to varying the backbone architecture and training schemes in current state-of-the-art few-shot learning techniques when used in human cell classification. To this end, the best technique identified in the first part of this study, EPNet, is used for experimentation. In particular, the study used 6 different network backbones, 5 data augmentation methodologies, and 2 model training schemes. Even with these additions, the overall test accuracy of EPNet decreased from 88.66% on non-medical datasets to 44.13% at best on the medical datasets. Third, this study presents future directions for using few-shot learning in human cell classification. In general, few-shot learning in its current state performs poorly on human cell classification. The study proves that attempts to modify existing network architectures are not effective and concludes that future research effort should be focused on improving robustness towards out-of-domain testing using optimization-based or self-supervised few-shot learning techniques.

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

Decision-making in a complex, dynamic, interconnected, and data-intensive industrial environment can be improved with the assistance of machine-learning techniques. In this work, a complex instance of industrial assembly line control is formalized and a parallel deep reinforcement learning approach is presented. We consider an assembly line control problem in which a set of tasks (e.g., vehicle assembly tasks) needs to be planned and controlled during their execution, with the aim of optimizing given key performance criteria. Specifically, the aim will be that of planning the task in order to minimize the total time taken to execute all the tasks (also called cycle time). Tasks run on workstations in the assembly line. To run, tasks need specific resources. Therefore, the tackled problem is that of optimally mapping tasks and resources to workstations, and deciding the optimal execution times of the tasks. In doing so, several constraints need to be respected (e.g., precedence constraints among the tasks, constraints on needed resources to run tasks, deadlines, etc.). The proposed approach uses deep reinforcement learning to learn a tasks/resources mapping policy that is effective in minimizing the resulting cycle time. The proposed method allows us to explicitly take into account all the constraints, and, once training is complete, can be used in real time to dynamically control the execution of tasks. Another motivation for the proposed work is in the ability of the used method to also work in complex scenarios, and in the presence of uncertainties. As a matter of fact, the use of deep neural networks allows for learning the model of the assembly line problem, in contrast with, e.g., optimization-based techniques, which require explicitly writing all the equations of the model of the problem. In order to speed up the training phase, we adopt a learning scheme in which more agents are trained in parallel. Simulations show that the proposed method can provide effective real-time decision support to industrial operators for scheduling and rescheduling activities, achieving the goal of minimizing the total tasks’ execution time.

Reduction of Lithium-Ion Battery Models with Degradation Dynamics: A Comparison of Techniques

This works studies the reduction of electrochemical lithium-ion battery cell models. Battery cell modeling is crucial to predicting battery behavior under different conditions and enables battery management system (BMS) control algorithms to ensure efficient and reliable operation. The finite difference method (FDM), commonly used for describing nonlinear battery dynamics, requires a high number of states to accurately capture the battery behavior. When computational resources are limited, model reduction is necessary to increase computational tractability and decrease runtime. The literature presents a variety of reduced-order modeling techniques, though these techniques do not commonly incorporate degradation dynamics. To address this gap, this study explores model reduction with the inclusion of degradation effects. Two methods are used to accomplish this. The first extends the application of the Padé approximation technique to incorporate degradation terms found in the boundary conditions of the single-particle model (SPM). This novel solution of the Padé approximation is compared against the Padé approximation solution that excludes degradation. The second uses optimization-based techniques to identify reduced-order models. The models produced are compared for different operating conditions of a lithium-ion battery cell. For the simulated case studies explored, optimization-based reduced-order models outperform Padé-based models for input conditions used for parameter optimization. However, the Padé-based models are more consistent with regards to negative electrode concentration accuracy for a separate validation input profile determined from a drive cycle's demanded current.

A Hybrid Visual-Based SLAM Architecture: Local Filter-Based SLAM with KeyFrame-Based Global Mapping.

This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approach.

Conservative Confidence Interval Prediction in Fused Deposition Modeling Process With Linear Optimization Approach

Abstract Regression models are widely used as data-driven methods for predicting a continuous target variable. From a set of input variables, regression models predict a deterministic point value for the target variable. But the deterministic point value prediction is not always sufficient because a target variable value often varies due to diverse sources of uncertainty. For instance, in the fused deposition modeling process, the inconsistent results of replications are associated with natural randomness, environmental condition, and noisy process parameters. The point value estimation is not sufficient to represent the variability in this kind of scenario. Instead of point estimation, the interval prediction of a target variable is more useful in this application. In this paper, linear optimization-based techniques are proposed to predict conservative confidence intervals for linear and polynomial regression models. Two linear optimization models, one for ordinary least squares (OLS) regression and the other for weighted least squares (WLS) regression, are proposed. The proposed methods are implemented on several datasets, including an experimental fused deposition modeling dataset to demonstrate the effectiveness of the proposed methods. The results show that the proposed method is useful for the fused deposition modeling process where the level of uncertainty or the lack of knowledge of uncertainty sources is high. The proposed method can also be leveraged to the Bayesian neural network (BNN), where the optimization techniques for interval prediction will be nonlinear optimization instead of linear optimization.

A dummy peak elimination based MPPT technique for PV generation under partial shading condition

The increased energy demand with recent advancement in technology has led the global energy producers to move towards solar energy. Partial shading is a phenomenon that can severely affect the production of the solar power in the photovoltaic system. With partial shading situation, the cascaded solar panels show numerous dummy peaks both in power–voltage (P–V) and current–voltage (I–V) curves, resulting in difficulty of extracting maximum power. A dummy peak elimination based MPPT technique is introduced in proposed work for accurate tracking of maximum power. The proposed dummy peak elimination based MPPT estimates the dummy peaks without imposing any high computational burden for the processor. The proposed technique has better dynamic response and is more compatible for real time application compared to existing optimization-based techniques. The proposed dummy peak elimination based MPPT is tested on a practical system with 3S configuration having three 249 W PV panels for different shading patterns, which displays acceptable outputs with good dynamic performance and better efficiency.