Subham Sahoo

Hybrid MVMO based controller for energy management in a grid connected DC microgrid

This paper deals with a grid connected DC microgrid. The DC microgrid which consists of a PV array and DC loads has been integrated into the main grid through a 3-phase inverter to take power from the grid during low generation and feed the grid during high generation. A control scheme to manage the power flow in DC microgrid by tuning the conventional proportional-integral (PI) controllers in the grid side converter using a swarm variant of hybrid mean variance mapping optimization (MVMO-SH) algorithm has been proposed and tested under critical disturbances such as insolation drop and load change. The designed controller has been tuned using two strategic methods - sequential tuning and simultaneous tuning. Additionally, MVMO-SH has been compared with genetic algorithm (GA) to validate better performance owing to the multi-parent crossover strategy of MVMO-SH. The optimal gains have further been tuned to develop a faster inverter control. The results confirm that the designed controller significantly reduces the DC voltage deviations as well as incorporates faster tracking of current during various disturbances.

Solution of economic load dispatch by evolutionary optimization algorithms — A comparative study

With various instances of load management situations brewing up in power systems, an economic portfolio for the unit generation and consumption scenario can provide some handy results. Economic load dispatch comments for the economic operation of specific units with a load connected at one end, and with instances of load change. The optimization algorithms have fathomably reasoned their impact in calculating the optimum cost solutions for any unit data. Previously, Genetic Algorithm (GA) & Particle Swarm Optimization (PSO) have been used to calculate as low a cost possible. Comparing the evolutionary algorithms have come out as a fruitful option since it always provide better results with all kinds of improvisation introduced. Another optimization algorithm Cuckoo Search algorithm was introduced which converge the values in a better time domain. In this paper, a comparative analysis of Cuckoo Search, GA & PSO has been studied for a 3-unit and 6-unit system ensuring a much better result possible than what was achieved previously.

A Multi-Objective Adaptive Control Framework in Autonomous DC Microgrid

This paper presents a multi-objective adaptive power management scheme for an autonomous dc microgrid which consists of a novel adaptive gain-based proportionate charging/discharging strategy in order to provide energy balancing between battery energy storage systems (BESSs) of different capacities. As BESSs are a key component to maintain reliability and stability in an autonomous microgrid, control measures for longevity of these units should be properly investigated. Following the recommended way of charging for BESSs, i.e., constant current/constant voltage (CV) charging, the conventional methods undergo mode switching using a floating reference value to control CV charging which does not guarantee zero current when the battery is fully charged. To account for this issue, a novel progressive de-rating control scheme for PV in a seamless fashion to carry out CV charging of BESSs is done using the adaptive mechanism introduced by model reference adaptive control scheme for optimized power management between sources and loads. Furthermore, emergency events such as load shedding and load following operation of PV have also been discussed in detail. The proposed control strategy is simulated in MATLAB/SIMULINK environment and tested on a 1 kVA FPGA-based experimental prototype to validate the control approach under different scenarios.

Power Quality Improvement of Grid-Connected DC Microgrids Using Repetitive Learning-Based PLL Under Abnormal Grid Conditions

This paper proposes a repetitive learning-based phase locked loop (RLPLL) to improve power quality of the grid-connected dc microgrids under distorted grid voltage in a weak grid. The harmonic component present in grid current in a high impedance network amplifies the distortion in voltage, which often leads to instability. Since the behavior of the conventional synchronous reference frame PLL (SRF-PLL) varies, owing to the proportional-integral gains constrained to harmonic rejection bandwidth ultimately leading to a sluggish response. However, RLPLL accommodates this limitation with a comparable dynamic performance and enhanced harmonic attenuation properties. This has been achieved by using a Lyapunov-based approach for harmonic estimation, which facilitates the periodicity and boundedness of the harmonic component to obtain an adaptive learning-based update. To deal with the computational burden, this paper also provides a low-computing alternative model of the proposed strategy. The dynamic response of RLPLL along with a comparative analysis with SRF-PLL is governed by many events directly affecting the dc voltage, which is critical for the operation of dc microgrids. Its performance is validated under different scenarios in a 1-kVA field programmable gate array-based experimental setup.

A detailed analysis of optimized load frequency controller for static and dynamic load variation in an AC microgrid

In the era of modern power systems, due to significant increase in the number of micro-grids (MGs), presence of sudden load perturbation, parameter uncertainties, structural variations; operational frequency highly deviates from the desired frequency. To reduce the frequency deviation and to maintain generation-load balance, power system needs an intelligent, flexible and optimized control. Classic controllers are unable to provide svelteperformance over a wide range of operations. To overcome this challenge, this paper presents a new online intelligent approach by using a combination of fuzzy logic controller with different optimization techniques for optimal tuning of proportional-integral (PI) based frequency controllers in the ac MG systems. This paper also presents a detailed comparative study between the static and dynamic load change and its performance is compared with CSO (Cuckoo Search Optimization) and PSO (Particle Swarm Optimization) based fuzzy PI controller, fuzzy PI controller and the Ziegler-Nichols PI control design methods. The systems are modeled and simulated in a user friendly MATLAB/Simulink environment.

An Adaptive Event-Triggered Communication Based Distributed Secondary Control for DC Microgrids

This paper proposes an adaptive event-triggered communication-assisted distributed secondary cooperative control strategy using a parameter projection law-based estimate of states to reduce communication burden. It overcomes the drawbacks of operating in an open loop manner between two triggering time instants in traditional zero-order hold-based event triggering schemes using a full state feedback control strategy to update the control input simultaneously. Moreover, real-time precision of information and model uncertainties is well dealt owing to the adaptive mechanism via an event-triggering condition designed using the Lyapunov technique to ensure the stability of the system. This strategy is used in tandem to achieve global average voltage regulation and proportionate load sharing in dc microgrids for various disturbances without sacrificing system performance. The proposed control strategy is simulated in MATLAB/Simulink environment and tested on a 500-W FPGA-based experimental prototype to validate the control approach under different scenarios.

A Distributed Finite-Time Secondary Average Voltage Regulation and Current Sharing Controller for DC Microgrids

This paper proposes a distributed finite-time secondary controller to achieve average voltage regulation and proportionate current sharing within a finite settling time for autonomous network of dc microgrids. It is employed by using a distributed finite-time control approach which maintains average voltage regulation of the system facilitating proportionate current sharing simultaneously by virtue of dynamic consensus between its neighbors. The proposed scheme ensures an improved performance over the conventional distributed secondary methods by reducing overshoots and chattering which is significant for critical operation of the loads. The proposed control strategy is simulated in MATLAB/Simulink environment to test link-failure resiliency, plug and play capability, and controller performance under communication delays within a tolerable upper bound on delay determined using time-delay analysis. Moreover the effect of variable time delays in different transmission medium is also simulated to test the practicality of the approach. This strategy is further tested on a 500-W FPGA-based experimental prototype to validate the control approach under different scenarios.

A decentralized adaptive droop based power management scheme in autonomous DC microgrid

This paper proposes a decentralized priority based adaptive droop control strategy to charge/discharge the batteries proportionately to achieve energy equivalencing in an autonomous DC microgrid. Due to low reliability index of autonomous DC microgrids, a proper control framework has been proposed in this paper which takes care of changing demand subject to the intermittent behaviour of solar PV array, slow dynamics of solid oxide fuel cell (SOFC) and battery longevity constraints. Additionally, DC grid code has been properly maintained throughout its operation. The proposed control strategy is validated in MATLAB/SIMULINK environment to demonstrate its effectiveness for various scenarios.

Improved Mean Variance Mapping Optimization for the Travelling Salesman Problem

This paper presents an improved Mean Variance Mapping Optimization to address and solve the NP-hard combinatorial problem, the travelling salesman problem. MVMO, conceived and developed by Istvan Erlich is a recent addition to the large set of heuristic optimization algorithms with a strategic novel feature of mapping function used for mutation on basis of the mean and variance of the population set initialized. Also, a new crossover scheme has been proposed which is a collective of two crossover techniques to produce fitter offsprings. The mutation technique adopted is only used if it converges towards more economic traversal. Also, the change in control parameters of the algorithm doesn’t affect the result thus making it a fine algorithm for combinatorial as well as continuous problems as is evident from the experimental results and the comparisons with other algorithms which has been tested against the set of benchmarks from the TSPLIB library.

An optimised design of controller for UPFC to improve the transient stability performance in power system

In the present scenario, due to the growing of energy demand it is necessary to maintain system stability. For this reason, various equipments based on the power electronics have been developed under the name of Flexible Alternating Current Transmission Systems (FACTS) devices; which are used in the last couple of years. Among all the facts devices Unified Power Flow Controller (UPFC) is the most versatile one in regard to control the power flow and to improve the transient stability of a system. The modelling of the controller plays a major role in the performance of the UPFC. The performance of UPFC for different mode of operation using PI controller is being studied. In this paper, a detailed comparative analysis is done, by taking performance criteria of UPFC controller into consideration, for optimal tuning of PI controller through various optimization techniques. By taking the settling time as the reference the performance is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search Optimization (CSO) which are so called population optimisation techniques, used for optimal tuning of PI controller. The UPFC is modelled in our user friendly software MATLAB/M-file environment.