Browsing by Author "Mishra, Puneet"

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Analysis of Parallel Control Structure for Efficient Servo and Regulatory Actions
(Springer, 2017-10) Mishra, Puneet
This paper investigates an intriguing issue about the tuning aspects of the parallel control structures. This parallel control structure essentially decouples the servo action from the regulatory action and provides an opportunity to the control engineer for separately deciding the ability of the controllers for servo and regulatory action. This paper provides a thorough comparative study and thereby suggesting an appropriate combination of tuning rules for achieving better efficiency of the control structure. Three different well accepted tuning rules viz. Ziegler Nichols, Direct Synthesis (DS) and Gain Margin Phase Margin formulae have been considered and a critical analysis of the control tuning rules combinations have been performed. The performance of considered tuning rules combinations is assessed on the basis of a transient response criterion, i.e., overshoot, an error-based criterion, i.e., Integral of time-weighted absolute error for both setpoint and disturbance rejection, and a measure of controller output aggression, i.e., Integral of absolute rate of controller output. On the basis of performed studies for a first-order plus dead time system, it may be inferred that DS–DS tuning rule combination provided superior performance among all the considered cases for nominal as well as plant-model mismatch case.
Chemiresistive urea sensor based on a composite film of Activated charcoal and Zinc Oxide
(IEEE, 2024) Mishra, Puneet; Gupta, Navneet; Panwar, Jitendra; Mathur, Hitesh Datt
Detection of nitrogen content in the form of urea is essential as it confirms its fertility for agricultural practices. Herein, a report on a simple microwave decomposition method for the synthesis of hybrid nanomaterial (Zinc Oxide and Activated Charcoal) that shows a maximum sensitivity of ~87% at 100mM urea concentration with response time and recovery time of 6 min and 80 min, respectively. The urea sensing mechanism with pre-adsorbed oxygen ions on the surface of the composite was verified by measuring the change in intensity of CO2 signal upon exposure to urea solution using FTIR. Thus, the composite film acts as a low-cost non-enzymatic chemiresistive urea sensor with good sensitivity and reproducibility.
A comparative study for flow control using SCIC and NPIC controllers
(IEEE, 2017) Mishra, Puneet
Flow control is essentially a very important part of the process control industries. The flow control loops often employ pneumatic control valves as the final control element. These control valves suffer from various nonlinearities and stiction is most common of these. Due to stiction effect in the pneumatic control valves, the commonly used proportional-integral-derivative (PID) controller introduces limit cycles in the flow control loops, which essentially deteriorates the productivity of the industrial environment. To curb such non sinusoidal oscillations in these control loops, recently two novel controllers have been proposed namely, Stiction Combating Intelligent Controller (SCIC) and Nonlinear Proportional Integral Controller (NPIC). These two controllers have been earlier thoroughly evaluated for flow control studies and are claimed to be very efficient for curbing the limit cycle behavior in the control loops. However, a comparative study between them is missing for the same, which would fill the void existing at present. This paper addresses the same issue and a comparative study between the two controllers is performed and presented in this paper for the setpoint tracking, disturbance rejection and parametric uncertainty problems.
Comparative study of some optimization techniques applied to DC motor control
(IEEE, 2014) Mishra, Puneet
Traditional tuning techniques for classical Proportional-Integral-Derivative (PID) controller suffer from many disadvantages like non-customized performance measure and insufficient process information. For the past two decades nature inspired optimization algorithms are efficiently being implemented for tuning of PID controllers. In this paper, four optimization methods namely Genetic Algorithm (GA), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE) and Cuckoo Search (CS) are studied and used to optimize the controller gains of a Proportional-Integral (PI) controller for set point tracking in speed control of a DC motor by minimizing Integral Time Absolute Error (ITAE). Hardware validation of the efficiency of above mentioned optimization algorithms is studied and presented. The plant under study is a DC motor control module (MS15) from M/S LJ CREATE™. M/S National Instruments (NI) based software and hardware components i.e. LabVIEW™ and its add-ons toolkit and data acquisition (DAQ) card has been utilized for the closed loop control in real time. The system identification is done in LabVIEW™ and then offline performance optimization is carried out in MATLAB™. The tuned gains are further used to study the run time performances in LabVIEW™ environment. This is done because MATLAB™ has very good optimization tools and on the other hand LABVIEW™ makes the measurement very easy. From the results obtained it can be clearly inferred that CS algorithm outperformed other algorithms studied in this paper, particularly in disturbance rejection.
Comparative study of some optimization techniques applied to Jacketed CSTR control
(IEEE, 2015-09) Mishra, Puneet
In this paper, the performance of four optimization techniques i.e. Grey Wolf Optimizer (GWO), Backtracking Search Algorithm (BSA), Differential Evolution (DE), and Bat Algorithm (BA) have been investigated for optimizing the scaling factors of fuzzy proportional-integral controller (FPIC). Jacketed continuous stirred tank reactor (CSTR) has been considered for step set-point and trajectory tracking of reactor temperature. The present work has been simulated in LabVIEW™. The performance of aforementioned algorithms has been evaluated by comparing the cost function Integral of Absolute Error for step set-point and trajectory tracking. On the basis of simulation results, it can be inferred that, GWO outperformed other optimization algorithms for all considered cases.
Cooperative relay beamforming in IDMA communication networks
(Sciendo, 2018-09) Mishra, Puneet
In this paper, a new combination of Interleave division multiple access (IDMA) and spatial diversity offered by cooperativerelay aided distributed beam forming is proposed. In the offered scheme communication strategy consists two steps. All usersbroadcast their message to relays in the first step and then relays amplifies and forward the information to the desireddestination. IDMA, which is popular non-orthogonal multiple access (NOMA) technique is used to combat the effect ofmultiple access interference (MAI) at relay as well as destination nodes. Each relay processed the signal to maintain the QoSof destination. The goal of this work is to find the appropriate beam forming weights by minimising the transmit power andwithout compromising the QoS in terms of SINR. However power minimization is not the convex problem, so semi-definiterelaxation is used to modify the problem in to semi-definite programming (SDP) problem and the conventional SDP problemsolver CVX is used for solution. The numerical explanation and simulation experiment of the proposed scheme shows theperformance improvements in terms of bit error rate.
A critical and comparative review of energy management strategies for microgrids
(Elsevier, 2022-12) Mathur, Hitesh Datt; Mishra, Puneet
Energy management (EM) can be defined as the process of monitoring, planning, optimizing, and saving energy to obtain an energy-efficient system. A microgrid (MG) is considered a sustainable energy system but due to the uncertain nature of renewable energy resources (RERs) and electrical load, it requires an optimal and efficient energy management system (EMS). It is vital for optimal usage of distributed energy resources (DERs) in a secure and reliable way. This function of EMS is performed by an advanced decision-making approach which is present in it. In recent years, many researchers have focussed on the development of advanced EM strategies for MG to establish a self-sustained MG. Therefore, a comparative study is required to have a 360° view of the domain EM in MGs. In this regard, this article presents a critical and comparative analysis of the EM strategies developed for the MG’s optimal decision-making along with their demand response strategies. The broader classification of EMS based on supervisory control, operating time platform, and an approach for making a decision is discussed along with their limitations and solutions. To manage the dynamic and intermittent nature of RERs and load, different uncertainty handling and modeling approaches are summarized. A systematic review strategy consists of some tasks is adopted by the authors, for example (i) Extraction of research articles related to EM in MG; (ii) Filtering out the important articles to prepare a relevant research article database (iii) Critically analyzing the EM strategy developed in each article. In a broader perspective, this paper presents an up-to-date systematic analysis of EM strategies for MGs developed by various researchers. Finally, some recommendations and future research directions are also suggested. We hope that this article will help the readers to map the conceptual structure of this research field
Cuckoo search implementation in LabVIEW
(IEEE, 2016) Mishra, Puneet
LabVIEW is one of the various programming platforms which is widely utilized by the researchers of fraternities form both industry and academia, due to its simple-to-use graphical programming environment and extra ordinary easy interface with the hardware. LabVIEW is equipped with various in-built toolkits to perform different measurement and control tasks. One of these tasks is solving an optimization problem which is often encountered by researchers from variety of fields viz. control engineering, civil design, machine design, digital signal processing, and economics etc. However, the standard LabVIEW package is equipped with only a single global optimization algorithm, i.e. Differential Evolution (DE) algorithm and there exists a need of other efficient global optimization techniques. This paper deals with same concern and an effort has been made to develop a widely established and practiced global meta-heuristic algorithm i.e. cuckoo search algorithm (CSA) in LabVIEW environment. To test the CSA implementation in LabVIEW environment, nine benchmark test functions have been used and a comparative study has been made with DE algorithm. It was found from the conducted studies that CSA was more efficient in solving optimization problems with better convergence rate and repeatability than DE.
A deep learning assisted adaptive nonlinear deloading strategy for wind turbine generator integrated with an interconnected power system for enhanced load frequency control
(Elsevier, 2023-01) Mathur, Hitesh Datt; Mishra, Puneet
The existing linear and quadratic deloading strategies with constant deloading factor, fail to effectively handle the nonlinear characteristics of WTGs. This work proposes a novel deep learning assisted adaptive nonlinear deloading (DL-AND) methodology based on a Newtonian interpolated polynomial for WTG integrated with an interconnected power system to provide effective load frequency control (LFC). The key feature of the proposed technique is its ability to adapt the deloading factor in accordance with wind speed to optimize the reserve power margin of the WTG. In this work, a deep learning-based recurrent neural network (RNN) with long short-term memory (LSTM) technique has been proposed for wind speed forecasting, as using a wind speed measurement device is expected to incorporate measurement lag, leading to the deterioration of the deloading operation. The proposed novel DL-AND technique for WTGs is used along with a fractional-order fuzzy-based PID (FFOPID) control structure as a supplementary controller for handling uncertainties in order to provide effective LFC. Further, Exhaustive simulation studies have been carried out to investigate the proposed technique and results show the effectiveness of proposed novel DL-AND strategy with FFOPID in terms WTG reserve power margin, frequency support and performance index for all the considered case studies.
Design of a dual-layered tilt fuzzy control structure for interconnected power system integrated with DFIG
(Wiley, 2021-07) Mathur, Hitesh Datt; Mishra, Puneet
The doubly fed induction generators based wind power plant is extensively used as a renewable energy source; however, their integration with interconnected power systems may cause equal challenges as well. Since such WPPs are decoupled from the grid frequency to operate at maximum power point tracking, hence they cannot participate in effective load frequency control and cause severe deterioration in the frequency regulation of the nonlinear IPS. To address these issues, this article proposes a novel dual-layered tilt fuzzy control structure (DLTFCS), which comprises of a fuzzy tilt integral derivative with a filter as the first layer working in coherence with a fractional order proportional derivative controller as the second layer, together with an inertial frequency support scheme for WPP. The DLTFCS is developed with an intention of efficient handling of nonlinearity and parametric uncertainty of considered IPS in the presence of WPP. Further, a curated hybrid objective function is also proposed, which consists of a weighted combination of integral of time-weighted absolute error and oscillatory measure with the help of an analytical hierarchy process and is optimized by a recently devised salp swarm algorithm. The control performance of the proposed controller has been validated by extensive simulation studies under various scenarios ranging from system parametric variations, uncertainties in generation rate constraint, and governor deadband to significant variations in wind penetration levels. Extensive comparative studies suggested that the proposed control structure provides superior performance over fractional order proportional integral derivative (FOPID) and Fuzzy FOPID controller.
Development of a Flower Pollination Algorithm toolkit in LabVIEW™
(IEEE, 2016) Mishra, Puneet
Flower Pollination Algorithm (FPA) is one of the latest evolutionary algorithms (EAs) inspired by the natural process of pollination of flowers. This Paper addresses the development of FPA toolkit in LabVIEW™, a versatile platform provided by National Instruments for test, measurement and control applications. It may be noted that LabVIEW™ has provided only one EA based optimization technique which is Differential Evolution (DE) algorithm named as Global Optimization.vi in its standard package. Since, several new and efficient techniques are available for optimization there is always a need to implement the best optimization technique in LabVIEW™ environment for the benefit of measurement and control engineers. The developed FPA toolkit has been tested on several benchmark test functions and its comparison is also carried out with the standard DE toolkit. Based on the investigations, it has been inferred that FPA toolkit results are better than DE toolkit in terms of convergence rate particularly for higher dimensional optimization problems.
Development of a Genetic Algorithm Toolkit in LabVIEW
(Springer, 2014-01) Mishra, Puneet
It is a well known fact that LabVIEW is one of the finest tools for measurement and control applications. Requirement of intelligent controller tuning methods like Genetic Algorithm (GA) has been felt at times in the LabVIEW environment as there is no standard LabVIEW GA toolkit supplied with the package. In this paper, a GA Toolkit developed in LabVIEW environment, has been presented. The developed toolkit is used for optimizing the gains of the PID (Proportional plus Integral plus Derivative) controller for the given performance indices of a closed loop system. For the purpose of tuning, the algorithm mimics the biological evolution and is used to find the suitable values of PID gains in order to improve the response of the given system. An integrated performance index comprising of rise time, settling time, overshoot, integral absolute error (IAE), integral square error (ISE), integral time weighted absolute error (ITAE) or a combination of these forms the objective function for the optimization. In this toolkit four selection methods, three crossover methods and three mutation methods have been incorporated. To test the developed toolkit a simulation example is also performed and results have been presented.
Development of a Grey Wolf Optimizer Toolkit in LabVIEW™
(IEEE, 2015) Mishra, Puneet
In this paper a Grey Wolf Optimizer (GWO) Toolkit developed in LabVIEW ™ environment, has been presented. Grey Wolf Optimizer (GWO) is inspired by Grey wolves (Canis lupus). The GWO algorithm, in nature, mimics the leadership hierarchy, and the hunting mechanism of grey wolves. LabVIEW ™ is a versatile tool for measurement and control applications and it is very popular among the industries. As Differential Evolution (DE) is the only optimization technique available in the LabVIEW ™ environment. The developed toolkit is examined on nine benchmark functions and results are compared with DE. A step by step systematic procedure for the toolkit development and a comparative study with the existing DE toolkit results has been presented in this paper.
Development of Ant Lion Optimizer toolkit in LabVIEW™
(IEEE, 2016) Mishra, Puneet
Metaheuristic or nature inspired optimization have evolved over time towards development of a general purpose optimizer. In this paper, a novel nature inspired algorithm, based on hunting mechanism of antlions, Ant Lion Optimizer toolkit is developed in LabVIEWTM environment. This paper provides an orderly procedure for development of toolkit including random walk of ants, building of traps, catching of prey and finally rebuilding of traps. LabVIEWTM is a highly productive development environment used largely in measurement and control applications. The only standard optimizer toolkit available in LabVIEW™ is Differential Evolution (DE) and therefore a need was felt to have an efficient optimizer. The toolkit so evolved in this paper when tested on different benchmark functions has performed adequately well over the DE toolkit. The results so obtained clearly prove that the submitted toolkit is able to provide superior results in terms of improved exploration, local optima avoidance, exploitation, and convergence.
Development of Backtracking Search Optimization Algorithm Toolkit in LabVIEW™
(Elsevier, 2015) Mishra, Puneet
In this paper a Backtracking Search Optimization Algorithm (BSA) toolkit has been developed in the LabVIEW™ environment. LabVIEW™ provides a graphical programming environment to design measurement and control applications. The development of BSA toolkit was motivated by the fact that only Differential Evolution (DE) toolkit was provided in LabVIEW™. Thus to design BSA toolkit, several modular virtual instruments have been developed for each BSA process. Developed BSA toolkit has been tested on several benchmark test functions and a comparative study with inbuilt DE toolkit has been performed, which shows results obtained from BSA toolkit are found superior to DE toolkit.
Development of Bat Algorithm toolkit in LabVIEW™
(IEEE, 2015-05) Mishra, Puneet
Meta-heuristic optimization algorithms are very useful tool for obtaining an optimal solution of engineering optimization problems. Bat Algorithm (BA) is one of the recent meta-heuristic algorithms. It has been claimed to be superior to its counter parts. On the other hand LabVIEW ™ is a versatile software tool being utilized for measurement and control applications in various engineering domains worldwide. The standard LabVIEW ™ package is only supplied with the Differential Evolution (DE) Toolkit for optimization. At times need was felt to develop a better optimization support in the LabVIEW ™ package. In this work a genuine effort has been made for this task and a BA Toolkit has been developed in LabVIEW ™ . The detailed development strategy, component descriptions and their interfaces and a comparison of the obtained results with the DE optimization toolkit has been presented in this paper for a set of classical benchmark functions.
Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration
(Springer Nature, 2024-07) Mathur, Hitesh Datt; Mishra, Puneet
This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.
Efficient IIR notch filter design using Minimax optimization for 50Hz noise suppression in ECG
(IEEE, 2015) Mishra, Puneet
Quality of Electrocardiogram (ECG) signal is very important for patient health diagnostics. ECG consists of frequencies ranging from 0.01 - 300Hz, while being a very small magnitude signal. ECG usually gets corrupted by the 50Hz power line interference (PLI). Thus, filtering out the PLI is a necessary task for the right diagnostics. In this work a second order efficient digital infinite impulse response (IIR) notch filter is designed to suppress PLI. For this task, Minimax optimization technique has been utilized to minimize the root mean square error (RMSE) defined as the difference of magnitude response of ideal and proposed IIR notch filter designs. The optimization process was able to set pole position close to the unit circle and the pass band gain is so chosen so as to have symmetrical performance resulting in attenuation at 50Hz notch of -260dB, a gain of 0.995 and -3dB bandwidth of 2.375Hz. The performance of the designed filter has been investigated theoretically as well as validated experimentally on a contaminated ECG signal and a pure sinusoid signal of 50Hz on field programmable gate array (FPGA) in the LabVIEW environment. In simulation, a power spectral density (PSD) of -30dB has been achieved for the simulated ECG signal originally having 4dB PSD at 50Hz and an attenuation of -30dB in PSD has been obtained for a pure sinusoidal signal. The designed filter was also implemented on FPGA a PSD of -26dB was obtained for ECG at 50Hz and -26dB for sinusoidal signal, respectively. Based on these studies it can be concluded that the proposed Minimax based notch filter design is an efficient one.
An efficient method for parameter estimation of a nonlinear system using Backtracking Search Algorithm
(Elsevier, 2018-06) Mishra, Puneet
Tank level systems are ubiquitous in process industries and exhibit a nonlinear nature. This nonlinear behaviour arises prominently due to nonlinear dependence of outflow rate on tank level and/or due to presence of other nonlinear elements in loop such as nonlinear actuators. It is essential to completely investigate the dynamics of system so as to generate an effective and accurate process model for obvious reasons. It may be noted that it is hard to find a unified model of a nonlinear system which can characterize the process in the entire operating range. To address this issue, this paper is a sincere effort to accurately identify a nonlinear tank level system with the help of a recently developed evolutionary algorithm i.e. Backtracking Search Algorithm (BSA). Two different optimization problems were created and solved using BSA to estimate the tank level system parameters and are termed as Method 1 and Method 2, respectively. The optimization was performed to minimize summation of absolute error (SAE) between experimental and simulated data yielding the parameters of model. During the training phase, Method 1 used only one data set and Method 2 used a combination of five different data sets of experiments at different operating conditions. From the results obtained through the experimentation on a hardware setup, it can be easily inferred that parameter estimation using Method 2 gives better identification than Method 1 by providing lower SAE values.
Enhancing the performance of a deregulated nonlinear integrated power system utilizing a redox flow battery with a self-tuning fractional-order fuzzy controller
(Elsevier, 2022-02) Mathur, Hitesh Datt; Mishra, Puneet
Load frequency regulation is one of the most vital and complex ancillary services in a deregulated power system. Increasing penetration from renewable energy sources in an integrated power system (IPS) further escalates the related control complexity due to a considerable decrement in IPS’s effective inertia. This may incur additional costs and can even lead to the destabilization of IPS. To overcome these problems in frequency regulation, this work proposes and investigates the use of an intelligent, direct adaptive control scheme, i.e., self-tuning fractional order fuzzy PID (STFOFPID) controller with and without the presence of a recently devised energy storage unit, i.e., the redox flow battery. The IPS’ efficacy with the STFOFPID controller is validated for various contracts in a deregulated operation mode for considered three area IPS. Extensive simulation studies are carried out, and detailed comparative studies have been drawn with conventional PID and fractional order PID controllers for load frequency regulation in Poolco, bilateral, and contract-violation mode of operation. Robustness analysis in terms of parametric variations in different nonlinearities present in a reheated thermal power plant is also carried out, and the efficacy of the STFOFPID controller is established using a thorough quantitative comparative analysis. The real-time digital simulation validation of the investigated control structure has been carried out on OPAL-RT 4150 based on Xilinx Kintex-7 FPGA board with INTEL multi-core processor.