Bonala Anil Kumar
@vaagdevi.edu.in
Assistant Professor, Electrical and Electronics Engineering Department
Vaagdevi College of Engineering
RESEARCH INTERESTS
Power Electronics, Renewable Energy Conversion Systems
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Vishnu Prasad Muddineni, Anil Kumar Bonala, and Thanuja Penthala
Springer Nature Singapore
Thanuja Penthala, Saravanan Kaliyaperumal, Vishnu Prasad Muddineni, and Anil Kumar Bonala
Springer Nature Singapore
Anil Kumar Bonala and Vishnu Prasad Muddineni
IEEE
The major concerns of Predictive Torque Control (PTC) of a 3-Level Neutral Point Clamped (3L-NPC) converter fed Induction Motor (IM) drive are weighting factor selection and computation burden. The implementation of PTC for a 3L-NPC fed IM requires torque, stator flux and DC-link voltage balancing as control objectives in the objective function. Hence, two weighting factors $\\lambda_{1}$ and $\\lambda_{2}$ are required respectively for the stator flux and DC-link voltage balancing in the objective function. To avoid the weighting factor for the stator flux, Predictive Flux Control (PFC) is considered in this paper. Further, selective switching states are considered to maintain the inherent neutral point balance and also to reduce the computational burdern. The total computations are limited to eleven switching states instead of twenty seven based on the stator flux location and operating mode of the drive with the 3L-NPC. The proposed method is compared with conventional PFC for 3L-NPC fed IM. The results demonstrate the effectiveness of the proposed control method under various operating conditions.
Vishnu Prasad Muddineni, Anil Kumar Bonala, Hareesh Kumar Yada, and Avudayappan Naraina
Springer Singapore
Vishnu Prasad Muddineni, Anil Kumar Bonala, and Srinivasa Rao Sandepudi
Institute of Electrical and Electronics Engineers (IEEE)
This article presents grey relational analysis (GRA)-based objective function optimization in predictive torque control (PTC) for induction machine. Selection of appropriate weighting factor in the objective function is one of the key aspects in the implementation of PTC. However, selection of suitable weighting factor in the objective function is a heuristic task. To address this issue, GRA method is implemented for the objective function optimization. In this approach, single-objective function is modified into two independent objective functions for stator flux and torque. A grey relational grade is used to identify the suitable control action in each sampling. A MATLAB/Simulink model is developed to validate the control algorithm under various operating conditions of the drive, and corresponding results are compared with experimental results.
Hareesh Kumar Yada, Bonala Anil Kumar, and Vishnu Prasad Muddineni
IEEE
This study presents a multifunctional inverter for a three – phase single – stage grid – connected SPV system with SO-SOGI based control algorithm. The SOSOGI based quadrature signal generation (QSG) estimates the magnitude of source voltage and load current signal and phase – locked loop (PLL) estimates phase angle of the grid voltage. This estimation is used to design the proper control algorithm which improves the power quality such as reactive power compensation, harmonic rejection; DC offset rejection and in addition to feed the SPV energy to load and grid. Perturb and Observe (P&O) based maximum power point tracking (MPPT) is used for estimating reference PV voltage signal and power. The SO-SOGI based controller tracks the signal even under distorted grid supply conditions such as voltage sag / swell, harmonics to improve the dynamic response. These multifunctional capabilities of the inverter make the system more efficient for feeding the SPV energy to grid. Various operating conditions are shown to validate the proposed control algorithm using MATLAB / Simulink.
Vishnu Prasad Muddineni, Anil Kumar Bonala, and Hareesh Kumar Yada
Springer Singapore
Vishnu Prasad Muddineni, Anil Kumar Bonala, and Srinivasa Rao Sandepudi
IEEE
This paper presents a modified objective function optimization for Predictive Torque Control (PTC) of induction machine based on Grey Relational Analysis (GRA) method. PTC offers better steady state and dynamic operating conditions on proper selection of weighing factor in the objective function. However, selection of appropriate weighting factor in the objective function is a heuristic task. To address this issue, Grey Relational Analysis (GRA) method is implemented in the objective function optimization to overcome the problems associated with weighting factor selection. In this approach, single objective function is modified into two independent objective functions for stator flux and torque. A grey relational grade is used to identify the suitable control action in each sampling. A MATLAB/Simulink model is developed to validate the control algorithm under various operating conditions and corresponding results are compared with experimental results.
Anil Kumar Bonala and Srinivasa Rao Sandepudi
Institution of Engineering and Technology (IET)
This study presents a single-stage grid-tied three-level neutral point clamped photovoltaic inverter with a centralised model-predictive decoupled active–reactive power control. The proposed centralised model predictive control (CMPC) incorporates the constraints of maximum power extraction, dc-link capacitor voltage balancing and active–reactive power tracking in a single objective function. The dc-link voltage of the inverter is regulated to its reference for extracting the maximum power. In order to eliminate the impact of reactive power exchange on floating dc-link voltage regulation, a decoupled active–reactive power control is used in the CMPC. Furthermore, a preference selective index-based dynamic weighting factor selection approach is introduced to maintain the relative importance between the power tracking and dc-link capacitor voltage balancing. The proposed control approach eliminates the outer dc-link voltage control loop and also, the empirical approach required for the selection of weighting factors. As a result, it ensures an optimal control action in each sampling period to improve the steady-state and dynamic tracking performance of the control objectives. The proposed control approach is experimentally verified by using a 1.2 kW laboratory-scale prototype and the results are presented to demonstrate its effectiveness compared to the classical proportional–integral-based model predictive control.
Jaripati Rajesh, K.S. Nisha, Anil Kumar Bonala, and Srinivasa Rao Sandepudi
IEEE
This paper presents a model predictive control of three-level boost converter(TLBC) interfaced PV array for bi-polar DC micro-grids. The proposed control extracts the maximum power from PV array and also balances the bi-polar voltages against the unbalanced loading. An individual pole voltages of micro-grid are regulated to their reference by using bi-directional buck-boost converter interfaced with Energy Storage System (ESS). Simulations are carried out with Matlab/simuink to observe the performance of the system for change in solar irradiance and load variations. The effectiveness of the proposed control is validated by comparing with classical PI controller and results are found to be satisfactory.
Anil Kumar Bonala, Srinivasa Rao Sandepudi, and Vishnu Prasad Muddineni
Institution of Engineering and Technology (IET)
A selective finite states model predictive control is proposed for a grid interfaced three-level neutral point clamped solar photovoltaic inverter. The proposed control approach eliminates the weighting factor selection for dc-link capacitor voltage balancing and reduces the computational burden for real-time implementation. The switching states required for the prediction and objective function optimisation are selected based on the position of reference voltage vector in the space vector plane, inverter current directions and the charge status of the dc-link capacitors. As a result, the selection of optimal switching state is fast, easy to implement and eliminates the selection of weighting factor for capacitor voltage balancing. The feasibility of the proposed control approach is verified through simulation and laboratory-scale experimentation. The results confirm that the proposed method attains the inherent dc-link capacitor voltage balance and also retains the dynamic and steady-state current tracking in comparison with the classical finite control-set model predictive control.
Vishnu Prasad Muddineni, Srinivasa Rao Sandepudi, and Anil Kumar Bonala
Informa UK Limited
Abstract A predictive torque control (PTC) technique is emerging as an effective alternative for induction motor drives because of its simple structure, ability to include control parameters directly into the cost function, and fast dynamic response. To implement PTC for induction motor drive, both stator flux and torque errors are predicted for each switching state of the converter and the combined error is determined with the help of cost function using a suitable stator flux weighting factor. However, the selection of suitable weighting factor is a tedious and complex task. In this paper, a simple additive weighting method is introduced in the cost function optimization to simplify the weighting factor selection process. To observe the effectiveness of the proposed control algorithm, both simulation and experimental results are presented for a two-level voltage-source-inverter-fed induction motor drive and compared with conventional PTC under various operating conditions. The proposed control technique achieves low-torque ripple along with reduced switching frequency.
Vishnu Prasad Muddineni, Srinivasa Rao Sandepudi, and Anil Kumar Bonala
Institution of Engineering and Technology (IET)
Finite control set predictive torque control (FCS-PTC) becomes popular for induction motor drives due to its simple structure and flexibility of including additional control parameters into the control law. However, primary concern of this control technique is the selection of suitable weighting factors in the cost-function. Usually, empirical method is used to select the weighting factors, which is time-consuming and heuristic process. In this study, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced in the cost-function optimization to simplify the difficulties involved in the weighting factor selection. This method selects an optimal control action, which is closer to positive ideal control action and far away from negative ideal control action. This ensures the selection of optimal control action in each sampling period based on the priorities given to control parameters in the cost-function. Further, to reduce the computational burden of proposed technique, a predefined set of switching states are used for the cost-function optimization based on previous optimal control action. Both simulation and experimental studies are carried out for a two-level voltage source inverter fed induction motor drive. These results are compared with conventional FCS-PTC technique to highlight the merits of proposed technique.
Vishnu Prasad Muddineni, Srinivasa Rao Sandepudi, and Anil Kumar Bonala
IEEE
Predictive torque control (PTC) is a kind of finite control set model predictive control (FCSMPC) technique and this method is one of the widely used modern control techniques for induction motor drives. This control method becomes popular due to its simple structure, fast dynamic response and ability to include additional constraints (control parameters) into the objective function of the control algorithm. However, weighting factors are used to maintain the relative importance of different control parameters in the objective function. The primary concern of implementing PTC is the selection suitable weighting factors in the objective function. In this paper, tuning of weighting factor is eliminated by selecting stator flux as the only control parameter in the objective function. Further, a predefined switching table based on stator flux location and torque error is used for the predictions of the control parameter. Hence, computational burden significantly reduces in proposed method compared to conventional PTC. The simulation results are presented for a 2-level voltage source inverter (VSI) fed 2.2 kW induction motor by using MATLAB/Simulink. These results are compared with conventional PTC and the merits of the proposed control algorithm are highlighted under various operation conditions.
Anil Kumar Bonala, Srinivasa Rao Sandepudi, and Vishnu Prasad Muddineni
IEEE
A model predictive current control (MPCC) of shunt active power filter (SAPF) requires tracking of distorted reference current based on the discrete mathematical model of the system. But the approximations considered in the development of mathematical model for the nonlinear system and also the uncertainty in the parameter values will deteriorate the performance of control algorithm. To enhance the current tracking performance, an improved MPCC for single-phase NPC converter based SAPF is presented in this paper. In the proposed control strategy, a mean squared predictive current error is used as a feedback in current tracking objective of the cost function. The optimal switching vector that minimizes the defined cost function is applied in the next sampling period. The robustness of the proposed control strategy for variation in grid feeder impedance and interfacing filter inductance is verified using matlab/simulink results. The results presented are compared with MPCC for absolute current tracking error and the percentage of total harmonic distortion (%THD).
Anil Kumar Bonala, Srinivasa Rao Sandepudi, and Vishnu Prasad Muddineni
IEEE
In this paper, a model based predictive current control (MPCC) with a modified synchronous detection technique (SDT) is developed for a 3L-NPC multi-functional inverter based solar photovoltaic (SPV) system. This 3L-NPC multi-functional grid tied inverter (MFGTI) perform maximum power point tracking, harmonic compensation, load balancing and power factor control. The proposed SDT consists of two-stages, fundamental component of sensed voltage is extracted using three 1-ph enhanced phase locked loops (EPLL) in the first stage, where as in the second stage, the reactive power injection is controlled by controlling the phase of the injecting currents to operate the point of common coupling (PCC) at the desired power factor. In order to achieve a fast dynamic control, a MPCC is employed for MFGTI. The discrete-time model of three-phase/3L-NPC MFGTI is used to predict the compensation currents for the possible 27 switching-states. The optimal switching-state that minimizes the objective-function is applied in the next sampling instant. The proposed control approach provides fast dynamic response for sudden change in active and reactive powers, high current tracking performance. Simulations are carried out using Matlab/Simulink and the results are presented to highlight the effectiveness of the proposed control technique.
Vishnu Prasad Muddineni, Srinivasa Rao Sandepudi, and Anil Kumar Bonalac
IEEE
PTC is a kind of finite control set model predictive control (FCS-MPC) technique gaining its attention in high-performance electric drive applications due to its intuitive concepts and simple structure. Further, additional control parameters (switching frequency, common mode voltage etc.) can be directly included into the objective-function of the control algorithm. However, suitable weighting factors are required to maintain the relative balance between diverse control parameters used in the objective-function. Stator flux and torque are used as control parameters in the implementation of conventional PTC and the empirical process is used to obtain the suitable weighting factor in the objective-function. To avoid this problem, simple additive weighting (SAW) method is introduced in the objective-function of PTC and this method simplifies the selection of weighting factors in the objective-function. To reduce the average switching frequency and computational burden present in the conventional PTC used for a 2-level VSI fed induction motor drive; four appropriate switching states are used to obtain the optimal control action instead of eight switching states of 2 - level VSI. Simulation results are presented for the proposed control algorithm and these results confirm the effectiveness of proposed control algorithm compared conventional PTC with reduced switching frequency.
Anil Kumar Bonala, Srinivasa Rao Sandepudi, and Vishnu Prasad Muddineni
IEEE
This paper presents a variable conductance factor (VCF) based control algorithm for single-stage grid connected solar photovoltaic (SPV) system. In this system, a two level, four-leg inverter is used for load balancing, harmonic mitigation and power factor correction in addition with the maximum power injection at the point of common coupling in a three phase four wire distribution system. The proposed VCF based control algorithm is used to calculate the effective conductance factor to inject maximum power and the reference currents to compensate load harmonics in the system. An incremental conductance based MPPT algorithm is used for calculating reference DC-link voltage and Lagrange's extrapolation based predictive deadbeat current control is used to track the reference currents obtained from VCF. The efficacy of the proposed controller is verified numerically by using Matlab/Simulink.
Vishnu Prasad Muddineni, Srinivasa Rao Sandepudi, and Anil Kumar Bonala
IEEE
Finite control set model predictive control (FCS-MPC) is one of the promising alternatives for classical controllers (PID, hysteresis, etc…) in electric drive applications. The inherent discrete nature of FCS-MPC is suitable for a converter fed direct torque control (DTC) of induction motor drive. This paper presents a comparative study between conventional DTC and finite control set model predictive direct torque control (FCS-MPDTC) for an induction motor drive. The conventional DTC offers fast dynamic response, however the presence of nonlinear controllers causes considerable torque and flux ripples. In this paper, a simple one step ahead FCS-MPDTC is used by considering stator flux and rotor flux as state variables. In this method, nonlinear controllers and heuristic switching table are replaced with a cost function based online optimization for the selection of suitable switching state. Further, a new cost function is introduced based on simple weighted sum method to eliminate the selection of weighting factors. The results are verified for both control techniques applied to a 6 kW squirrel-cage induction motor under different operating conditions using MATLAB/Simulink. Proposed method resulted in improved steady state and dynamic response along with reduced torque and flux ripples compared to its counterpart.
Vishnu Prasad Muddineni, Anil Kumar Bonala, and Srinivasa Rao Sandepudi
Institution of Engineering and Technology (IET)
Predictive torque control (PTC) is one of the widely used modern control techniques for induction motor drives due to its merits such as; implementation is straightforward and direct inclusion of control parameters into the cost-function is possible. However, the main drawback of this technique is the selection of appropriate weighting factor in the cost-function. In this study an attempt is made to simplify the weighting factor selection by using VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method and the cost function of PTC is modified accordingly. Usage of VIKOR method in the cost-function optimisation introduces the compromise ranking to select an optimal control action. Both simulation and experimental results are carried out for a two-level voltage source inverter fed induction motor drive with the proposed control technique and the results are compared with conventional PTC technique. These results confirm that proposed method retains the dynamic response achieved by the conventional PTC along with reduced torque ripple and switching frequency.