Prof.(Dr.) Ajit Kumar Barisal
@cet.edu.in
Professor in Electrical Engineering
College of Engineering and Technology, Bhubaneswar
RESEARCH INTERESTS
research interests include economic load dispatch, Hydrothermal Scheduling, alternative energy power generation and soft computing applications to different power system problems
Scopus Publications
Scopus Publications
Neelakantha Guru, Manas Ranjan Nayak, Ajit Kumar Barisal, and Samarjit Patnaik
Elsevier BV
Mrutyunjaya Mangaraj, Jogeswara Sabat, and Ajit Kumar Barisal
Walter de Gruyter GmbH
Abstract This article proposes an adaptive Kernel-Hebbian least mean square (KHLMS) controller for a dual voltage source inverter (VSI). The recommended topology consists of a distributed energy resource (DER) supported VSI called main VSI (MVSI) and split capacitor supported VSI termed as auxiliary VSI (AVSI). Both the MVSI and AVSI are used to serve the shunt compensation when DER is not integrated with MVSI. The DER scenario is considered to suppress the active power flow shortage in the utility grid. Here, optimal active power flow control (OAPFC) is managed by MVSI and shunt compensation is achieved by AVSI during DER operated mode. Hence, a dual VSI based distribution static compensator (DSTATCOM) facilitates the configuration merits such as reduction in system downtime cost, filter rating switching stress etc. Supremacy of both the neural network (NN) based controller and topology is presented by comparing VSI (called AVSI) in the context of harmonic reduction in source side, voltage balancing, power factor (PF) enhancement, better voltage regulation and OAPFC. The experimental results are obtained through field programmable gate array (FPGA) based hardware units which exhibit radical improvement in the power quality (PQ) conferring as per the international standard grid code (IEEE-519-2017).
Jogeswara Sabat, Mrutyunjaya Mangaraj, and Ajit Kumar Barisal
Springer Science and Business Media LLC
Jogeswara Sabat, Mrutyunjaya Mangaraj, and Ajit Kumar Barisal
Springer Science and Business Media LLC
Jogeswara Sabat, Mrutyunjaya Mangaraj, Ajit Kumar Barisal, Praveen Kumar Yadav Kundala, and Rohan Vijay Thakur
Institute of Advanced Engineering and Science
This article presents the dual operation of distributed energy resources (DER) integrated impedance source inverter (DI-ZSI). The distribution grid, DER and variable nonlinear load are operating on two modes. In mode-1, power generated by the DER is zero or less then the load requirement and the inverter act as a voltage source inverter (VSI) for shunt compensation only. But, in mode-2, power generated by the DER greater than the load requirement and operates as a DI-ZSI based distributed static compensator (DSTATCOM). In this scenario, it not only acts as a shunt compensator but also inject active power to the distribution grid. An accurately tuned proportional integral with adaptive least mean square (ALMS) controller is used to generate the switching signals of inverter switches. The DI-ZSI performs stable operation in the distribution grid over a variable non-linear loading. A field programmable gate array (FPGA) SPARTAN-6 controller is used to develop the proposed system. Experimental results from DI-ZSI and VSI under variable loading highlighted the superiority of the DI-ZSI as per guidelines imposed by IEEE-2030-7-2017.
Krushna Keshab Baral, Prakash Chandra Sahu, Ajit Kumar Barisal, and Banaja Mohanty
Springer Science and Business Media LLC
Soudamini Behera, Sasmita Behera, and Ajit Kumar Barisal
Springer Science and Business Media LLC
Rachita R. Sarangi, Prakash K. Ray, Ajit K. Barisal, and Asit Mohanty
IEEE
With the penetration of DERs (Distributed Energy Resources), conventional transmission and distribution systems are getting increasingly replaced with converter based topologies along with localized transfer of power to loads. These are extremely dynamic systems, exposed to numerous intermittent sources of energy such as solar power, wind energy etc. The converters which interface the power generation sources with the load have to tightly maintain the bus voltage in order to maintain supply reliability and power quality. They also need to be resilient enough when faced with faults and disturbances in the system. However, these power converters which consist mostly of power electronic components which have very high switching frequencies, when faced with an unprecedented condition might give in to failures due to their low tolerance to very high currents that might cause damage to these components. These kinds of faults need to be accurately detected and isolated within time then to create a smart, reliable, energy efficient power system.
Rachita R. Sarangi, Asit Mohanty, Prakash K. Ray, Ajit K. Barisal, and Suvendu M. Baral
Springer Nature Singapore
Kausik Nanda, Neelakantha Guru, Ajit kumar Barisal, and Siddhanta Pani
IEEE
The performance improvement of multilevel inverters has attracted the attention of researchers. The conventional topologies of multilevel inverters require many power-electronic switches which leads to a significant amount of energy being lost as a cause of high-frequency switching. Hence, the trade-off between the losses occurring in the switches of the converter and the efficiency has introduced the concept of reduced component count new multilevel inverter topologies. This paper proposes a modified single-phase inverter, a development of a conventional H-bridge structure. The proposed structure can generate a 15-levels of single-phase voltage output. The proposed inverter is simulated using MATLAB/Simulink software. A simple PWM strategy i.e., Selective Harmonic Elimination is employed to eliminate the lowest-order significant harmonics. Harmonic elimination and THD minimization are nonlinear problems that can be effectively handled by metaheuristic approaches. In this paper four metaheuristic techniques i.e., Teaching Learning Based Optimization, Flower Pollination Algorithm, Modified Particle Swarm Optimization & Invasive Weed Optimization are implemented to obtain the switching angles of the SHE scheme for THD minimization and IWO beats the output of others.
Neelakantha Guru, Manas Ranjan Nayak, Ajit Kumar Barisal, and Samarjit Patnaik
Informa UK Limited
Rojaline Priyadarsini, Archana Nayak, and Ajit Kumar Barisal
IEEE
A slight impulsive load change in any zone of an interconnected power system will cause fluctuations in frequency and power in all zones. The main intention of load frequency control (LFC) is to stabilize the actual frequency and desired output power (MW) in the interlinked power system, by managing the variations of tie line power in controlled areas. Inherently, the LFC scheme constitutes a suitable control system for connected power systems. The control system has the ability to restore the local frequency and can connect the power to the initial setting point or very close to it after the load is moved by the help of standard controller. In my study, an AI-based controller has been used to analyze the load frequency control in a three-zone interconnected thermal hydro power generation system dynamically. The proposed idea makes use of advanced controlling methods using PI, PID and Fuzzy logic controllers for an interconnected hydrothermal heating power generation system in three areas system. The controller parameters which are made up here is based on the particle swarm optimization (PSO) technique, that uses an objective function called the integral time absolute error (ITAE) to control the deviation in frequency The performance simulation of the controller is done by using MATLAB2016b and by comparing the proposed fuzzy logic- based solution with PI and PID under the same conditions. By proper comparison among these methods, it is clearly noticeable that fuzzy logic controller performs better than other two approaches. The results of simulation are summarized and comparison analysis of the performance is done in terms of peak overshoot and settling time.
Soudamini Behera and Ajit Kumar Barisal
IEEE
In this paper Hydrothermal Scheduling (HTS) problems is solved using an advance Grey Wolf Optimizer algorithm named as Quasi Oppositional Grey Wolf Optimization (QOGWO) algorithm through cascaded reservoirs. Instead of pseudo-random numbers quasi-opposite numbers are used to initialize population in the proposed QOGWO method so that the convergence rate of GWO increases. Three intelligent algorithms such as GWO, QOGWO and Social network search (SNS)are compared in this complex hydrothermal system with many constraints. The feasibility of the projected approach is demonstrated in a multi-chain cascaded hydrothermal system with four interconnected hydro systems. Water transportation delay between interconnected reservoirs, Prohibited Discharge Zones (PDZ), Valve Point Loading (VPL) are considered in different combination in three cases. The PDZs of reservoirs of hydro plants have taken into account to ensure the viability of the projected method. The scheduled hourly rates of water flow founded by the projected QOGWO. The technique put forth with established superior to many recent findings for the STHS problems with increased complexities.
Aishwarya A Jena, Suvendu M. Baral, Shanti S. Rath, P. Ray, Ajit Kumar Barisal, Rachita R. Sarangi and A. Mohanty
Because of the penetration of renewable energy into the power system, the system will undergo significant changes, not only in terms of performance but also in terms of relay protection settings. It will disrupt existing relay coordination, perhaps preventing these relays from detecting a fault that is intended to be inside their protective zone. A considerable number of relay and circuit breaker sets must be placed in order to provide appropriate reliable protection and maintain the smooth operation of the electrical system. A suitable coordinated protection system with a predetermined sequence of action is necessary to isolate the malfunctioning zone from the remainder of the network. The primary protection relay must operate at the set time. If case of failure of primary protection, the backup protection shall be coordinated to isolate the faulty part. Using ETAP software, protection coordination is simulated in real time and the results are presented.
Neelakantha Guru, Abinash Prusty, Siddhanta Pani, Kausik Nanda, and Ajit Kumar Barisal
IEEE
The Multilevel Inverter (MLI) comes in large range of levels and the study of MLI took an acceleration in recent time. MLI has wide range of use in moderate voltage and large power applications. For utilization, the inverter must satisfy the voltage requirement as desired by the load, so controlling the response of the inverter is a matter of concern. In this paper, intelligent algorithm-based Proportional Integral (PI) controller is implemented for nourishing the output response of the inverter. The Regulation of Inverter output voltage is done by minimizing the “Integral Time Absolute Error” (ITAE). Here ITAE is considered as the objective to be minimized using stochastic optimization techniques. Optimization techniques like, “Particle Swarm Optimization” (PSO), “Genetic Algorithm” (GA) and “Artificial Bee Colony” (ABC) are implemented and compared. A seven level H-Bridge based Cascaded Multi-level Inverter (HBC MLI) is simulated using “Level Shifted Pulse Width Modulation” (LS PWM) technique. ABC optimization technique proved to be better than the other techniques in terms of better fitness.
Siddhanta Pani, Neelakantha Guru, Debendu Puhan, and Ajit Kumar Barisal
IEEE
The multilevel inverters are of wide use in recent era. Conventional multilevel inverters (MLI's) used previously has greater numbers of switches which gives enormous switching losses. Requirement for bargain in losses and elevated efficiency, reduced switch topologies came into picture. The critical task of an inverter is to obtain pure sinusoidal output voltage. Obtaining such waveform can be carried out with the resource of the use of disposing of the lower order harmonics. As the stages of the inverter will grow the number of Harmonics eliminated moreover will grow. Selective Harmonic Elimination (SHE) technique is employed to estimate the switching angles. The topology of eleven level MLI is proposed to reduce the switches, switching losses, total harmonic distortion and converter cost. The THD is itself a non-linear problem and can be effectively handled by meta heuristic techniques such as PSO, DE and TLBO. In the comparative study of the meta heuristic techniques, TLBO outperforms from the other techniques.
Ashok Krishnan, Prakash K. Ray, and A. K. Barisal
IEEE
An optimal day-ahead scheduling formulation for a typical multi energy system (MES) is presented in this paper. A typical MES may include components such as combined cycle gas turbines (CCGTs), renewable energy generators (REGs), battery energy storage systems (BESSs), boilers and thermal energy storage systems (TESSs). The proposed formulation includes the startup and shutdown trajectories of the CCGT components such as gas turbines (GTs), boilers and steam turbines (STs). The proposed formulation also includes a practical, load management scheme which optimally schedules the flexible components in the system including the electrical pumps, the thermal loads and the interruptible loads (ILs), thereby reducing the total operating cost of the system. The proposed formulation’s efficacy is demonstrated using a day-ahead scheduling problem solved under four scenarios.
Soudamini Behera, Sasmita Behera, and Ajit Kumar Barisal
Informa UK Limited
Soudamini Behera, Sasmita Behera, Ajit Kumar Barisal, and Pratikhya Sahu
Emerald
Purpose Dynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA). Design/methodology/approach Different model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically. Findings The efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established. Research limitations/implications Cost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution. Practical implications The proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible. Social implications As the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact. Originality/value In this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.
Jagannath Paramguru, Subrat Kumar Barik, Ajit Kumar Barisal, Gaurav Dhiman, Rutvij H. Jhaveri, Mohammed Alkahtani, and Mustufa Haider Abidi
Computers, Materials and Continua (Tech Science Press)
Subhranshu Sekhar Pati, Prajnadipta Sahoo, Santi Behera, Ajit Kumar Barisal, and Dillip Kumar Mishra
Springer Singapore
Abhishek Kuanar, Debasish Behera, and Ajit Kumar Barisal
IEEE
The Economic Load Dispatch (ELD) is described as the process of allocating generation level to the generating units, so that the system load is supplied entirely and most economically. In this paper we have implemented an efficient constriction factor-based Particle swarm optimization (CFBPSO) method to minimize the total fuel cost of the power generating units while satisfying the load demand & several operational constraints. The proposed method is applied to 3-unit and 6-unit power generating systems to find the best reduced fuel cost required for generation and to minimize the losses. It is a metaheuristic algorithm based on the concept of swarm intelligence and with the use of constriction factors. The results obtained through CFBPSO are then compared with Cuckoo Search Algorithm (CSA) method to prove its effectiveness over CSA method.
Soudamini Behera, Sasmita Behera, Ajit Kumar Barisal, and Sonam Pradhan
IEEE
There are two industries reviewed as the chief sources of emissions: transport and electric power. Various strategies like incorporation of Plug-in Electric Vehicles (PEVs), Renewable Energy Resources (RERs) have been proposed earlier to reduce the exponential increase of this GHGs emission. This paper gives a detailed approach of combination of both economic and emission dispatch (CEED) to ensure the efficacy of utilizing PEVs and RERs from different scenarios. Two test cases are taken into consideration to investigate the efficiency of the projected model. To get a better and practical assessment, the cost function due to wind energy source is introduced in this dispatch problem calculation. The result shows the fact that the utilization of PEVs does not lessen the total emission from the industries effectively. Therefore the integration of both PEVs and RERs with the 20 unit thermal system is required for the desired output.
Somanath Mishra
Institute of Advanced Scientific Research
Somanath Mishra, A. K. Barisal, and B. Chitti Babu
Informa UK Limited
ABSTRACT In this paper, Invasive weed optimization (IWO) algorithm is proposed for testing automatic generation control (AGC) of multi-area power systems. Initially, a two-area multi-source power system interconnected by a DC link is considered and IWO is employed to optimize the proportional integral derivative (PID) controller for AGC employing a modified objective function. The superiority of the proposed approach is shown by comparing the results with recently published optimal algorithms for the similar system under study. The comparison is made using various performance criteria such as integral of time multiplied absolute error (ITAE), minimum damping ratio (MDR) of dominant eigenvalues, settling times and maximum overshoots. Furthermore, sensitivity analysis is performed to show the robustness of the proposed approach by varying the system parameters, operating load conditions from their nominal values as well as size and locations of disturbance. The proposed approach is also extended to three area system considering both thermal and hydro units with generation rate constraints (GRC) and the superiority of the proposed approach is shown by comparing the results of Adaptive Neuro Fuzzy Inference System (ANFIS), hybrid Bacteria Foraging Optimization Algorithm with Particle Swarm Optimization (hBFOA–PSO) approaches for the identical interconnected power system.