Maximizing energy tracking in photovoltaic grid-connected system through modified tunicate swarm algorithm – control on environmental effect of irradiance conditions

ABSTRACT

Photovoltaic (PV) systems are mostly utilized in all fields of applications, especially for remote electrification purpose. Irradiance change during the day, and the conditions are more dynamic at cloudy days. Environmental conditions are a challenge/dynamic task and require individual control methods for maximum tracking of the available power. Maximum Power Point Tracking (MPPT) methods are designed through power electronic converters, which supply the PV systems to the load. Maximum Power Point (MPP) conditions of PV system with proposed Modified Tunicate Swarm Algorithm (md-TSA) effectively tunes the controller parameters by the help of voltage and current control. Power control with I/V control is almost improved up to 3.2% as compared with existing methods. Testing of md-TSA has been validated during uniform irradiance and different partial shading conditions. Proposed md-TSA tracking control vector is validated with Positive Conductance Region (PCR) and Negative Conductance Region (NCR) in all maximum energy tracking cases. In a propped work, Gravity force of will be modified and determined modified gravity force. Hence control of md-TSA will to meet $dP/dV$ and ($I+dI/dV)$ on both PCR and NCR for validating MPP. Proposed md-TSA has been validated for 50% loading and 100% loading conditions with the control vector. Quantitative data according to variations of loading, the voltage control vector can able improves the performance up to 6–8%. Also quantitative data according to variations of irradiance levels, the control vector of module current will improve the performance 5–10% and explored in test condition-1, 2 of results. By the proposed md-TSA, islanding has been tested with added control to voltage thresholds and hence proper switching pulses of converter are initiated. Here islanding switching $t_{sw}$ has been considered either ‘0’ or ‘1’ for test event validation. Switching time signal $t_{sw}$ have determined for switching instants $S\ge 0$, $S<0$ and validated based on quantitative data variations $dP/dV\ge 0$ on PCR and $dP/dV<0$ on NCR of MPPT. Proposed md-TSA verified over a PSO, fuzzy and conventional MPP methods and control taken as per 2–6%effective control of energy extraction estimated during quantitative data of load and irradiance level.

KEYWORDS:

• Irradiance
• swarm algorithm
• positive conductance region
• negative conductance region and MPPT control

Nomenclatures and abbreviations

MPPT: Maximum Power Point Tracking, md-TSA: Modified Tunicate Swarm Algorithm, PCR: Positive Conductance Region, NCR: Negative Conductance Region. ${\tau }_{OC-V}$: MPPT voltage control variable, ${\tau }_{OC-I}$: MPPT current control variable. $i_{PV}$, $v_{PV}$: current and voltage of PV array, ${\vec{G}}_r$: gravity force, $\vec{A}$: Search agent vector, $\vec{F}$: Water force in deep sea, $F{S}_d$: optimum position of food source, $t_{sw}$: switching pulse.

Acknowledgments

Sincerely thanks and acknowledge AICTE, Govt. of India for the financial assistance under MODRODS scheme of File No. 84-165/RFID/MODROB/Rural/Policy-1/2019-20.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

K. Harinadha Reddy

K. Harinadha Reddy was born in India on 02 July, 1974. He received B. E. degree in electrical and electronics engineering from K. U. in 1997, India. He completed M.Tech degree in electrical and electronics engineering from J. N. T. University, Kakinada Campus, India in the year 2006. He obtained PhD degree in electrical power systems from Andhra University Campus in the year 2012. He is professor with department of Electrical and Electronics Engineering, Lakireddy Bali Reddy College of Engineering (A), Mylavaram, Krishna Dt – 521230, AP, India. He is also reviewer for reputed journals like IEEE transactions and other high impact factor journals. His research interest areas are power and energy systems, integrated renewable energy systems, islanding detection, power electronic conversion with power converters, electrical drives, fuzzy systems, neural nets, AI, and machine learning techniques for electrical power and energy applications.