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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Sreenivasan, Pushpa
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Control & Systems Engineering Electrical & Electronics Engineering

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Improved load frequency control in dual-area hybrid renewable power systems utilizing PID controllers optimized by the salp swarm algorithm Sreenivasan, Pushpa; Dhandapani, Lakshmi; Natarajan, Shanthi; Adaikalam, Arul Doss; Sivakumar, Amudhapriya
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1711-1718

Abstract

In this study, we utilize the salp swarm algorithm (SSA) to optimize proportional integral derivative (PID) controller gains for load frequency control (LFC) in a multi-area hybrid renewable nonlinear power system. Incorporating generation rate constraints and dead-bands into the governor model, we examine system nonlinearities. Performance evaluation employs both single- and multi-objective functions, with actual sun irradiation data validating SSA-PID controllers' efficacy in managing renewable energy source uncertainties. Comparing with alternative optimization techniques across various operational scenarios reveals the SSA-PID controller's 15% improvement in dynamic response time. The findings suggest SSA enhances LFC dynamic response in hybrid renewable power systems, with potential generalizability. These results underscore SSA's utility in addressing system complexities, offering implications for improved stability and efficiency across renewable energy integration scenarios.
Leveraging machine learning for sustainable integration of renewable energy generation Sreenivasan, Pushpa; Ganesan, Keerthiga; Fawad, Iffath; Sureshkumar, Sathya; Dhandapani, Kirubakaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1347-1355

Abstract

Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.
Enhancing power quality in solar-wind grid-connected systems through soft computing techniques Kathirvel, Jayashree; Sreenivasan, Pushpa; Mohammed, Soni; Sethi, Rabinarayan; Syamala, Maganti
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2493-2500

Abstract

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.
Smart solar maintenance: IoT-enabled automated cleaning for enhanced photovoltaic efficiency Ramalingam, Puviarasi; Kathirvel, Jayashree; Adaikalam, Arul Doss; Somasundaram, Deepa; Sreenivasan, Pushpa
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp14-19

Abstract

This innovative project aims to increase the effectiveness and user experience of solar panel systems by introducing a state-of-the-art dust and speck removal system. Leveraging cutting-edge technology, the system demonstrates a remarkable 32% increase in power output compared to dirty solar panels. The approach is characterized by its reliance on the universe as the system controller, reducing the need for manual intervention and minimizing the workforce required for panel cleaning. The proposed timed system utilizes water and wipers, facilitated by internet of things (IoT) technology, microcontrollers, and sensor modules for efficient and automated operation. An Android application provides user control and notifications about ongoing processes. The system’s adaptability for various settings is emphasized, offering a portable solution. The smart IoT based automatic solar panel cleaning ensures reliable performance, underscoring the project’s commitment to improve scalability, cost-efficiency, performance, integrity, and consistency.
Enhancing voltage stability in active distribution networks through solar PV integration Dhandapani, Lakshmi; Sreenivasan, Pushpa; Murugan, Sangeetha; Maria, Helaria; Banerjee, Sudipta
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp1137-1146

Abstract

Solar PV's explosive expansion is changing distribution networks and posing new problems, such as bidirectional power flow, unstable voltage, and power quality problems, particularly in networks with low X/R ratios. Abrupt changes in voltage are difficult for conventional voltage control techniques like shunt capacitors and on-load tap changers (OLTCs) to handle. IEEE Standard 1,547 has little efficacy in such networks, despite the fact that PV inverters may provide reactive power. This paper suggests a real-time coordinated control approach to improve voltage regulation by combining PV inverters, OLTC, and battery energy storage systems (BESS). Reactive power from PV inverters is prioritized to lower operational expenses and reliance on BESS. Better voltage stability, a decrease in BESS energy processing from 9400.3 kWh to 1701.87 kWh, and a reduction in OLTC activities are the outcomes. Rural networks gain from the strategy's ability to support smaller, more affordable BESS units’ voltage sensitivity analysis, and ideal BESS sizing may be investigated in future studies.
Artificial raindrop algorithm for control of frequency in a networked power system Dhandapani, Lakshmi; Sreenivasan, Pushpa; Batumalay, Malathy
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp1116-1123

Abstract

Load frequency control (LFC) evaluates the net changes in generation by continuously monitoring tie-line flows and system frequency required relying on load changes. It adjusts generator set points to minimize the area control error's (ACE) time-averaged value. ACE is regarded as a controlled output of LFC. Previous research focused on customary power systems like hydro-hydro, thermal-thermal, and hydro-thermal configurations. This current development study introduces the hybrid PV and dual thermal system interconnected systems for LFC analysis. The research evaluates LFC performance with different controllers, considering parameters such as maximum peak overshoot (Mp), maximum undershoot (Mu), settling time (Ts), and peak time (Tp). Controllers, including proportional integral (PI), anti-windup PI, fuzzy gain scheduling PI, and A cutting-edge algorithm generating fake raindrops are used for minimize ACE. The analysis introduces various load perturbations to observe controller performance in interconnected power systems. Both PV-thermal-thermal and thermal-thermal-thermal systems exemplify innovative approaches to energy management that bolster energy efficiency and sustainability. By integrating these advanced systems, we can make significant strides towards achieving global sustainability goals and promoting a cleaner and support energy efficiency for the future.
Co-Authors Adaikalam, Arul Doss Banerjee, Sudipta Batumalay, Malathy Dhandapani, Kirubakaran Dhandapani, Lakshmi Fawad, Iffath Ganesan, Keerthiga Kathirvel, Jayashree Maria, Helaria Mohammed, Soni Murugan, Sangeetha Natarajan, Shanthi Ramalingam, Puviarasi Sethi, Rabinarayan Sivakumar, Amudhapriya Somasundaram, Deepa Sureshkumar, Sathya Syamala, Maganti