Internet of Things

IOT Based Intelligent Energy Monitoring of Grid connected Hybrid Renewable Energy Systems

November 10, 2024

Introduction

The integration of Internet of Things (IoT) technology into modern electrical grids has ushered in a new era of smart energy management and enhanced power quality. As the complexity of power systems continues to grow with the incorporation of renewable energy sources like Solar Photovoltaic (PV) and Wind Generating Systems (WGS), traditional methods of power quality control often fall short, leading to inefficiencies and potential disruptions. To address these challenges, this article presents an innovative IoT-based Smart Grid energy surveillance system that leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize power distribution and control.

The proposed framework combines the strengths of Artificial Neural Networks (ANNs) and Fuzzy Logic Systems to enhance the monitoring and management of hybrid renewable energy systems. By integrating a Wireless Sensor Network (WSN) for real-time data collection and analysis, the system enables precise tracking of electricity usage, facilitating improved energy management and cost reduction for both consumers and utility providers.

Key Highlights of the Proposed Approach:

Integration of IoT and ANFIS technologies to create an intelligent power monitoring and control system
Utilization of a Wireless Sensor Network (WSN) for real-time data acquisition and analysis
Optimization of power generation from Solar PV and Wind Generating Systems through ANFIS-based control
Seamless integration of renewable energy sources into the smart grid infrastructure
Enhanced power quality, energy efficiency, and cost savings through intelligent energy management

Challenges in Modern Power Grids and the Role of IoT

Traditional power grids have struggled to keep pace with the increasing complexity of modern energy systems, particularly due to the integration of renewable energy sources. Factors such as fluctuating solar irradiance, variable wind speeds, and dynamic load demands have often led to inefficiencies and power quality issues, undermining the reliability and stability of the grid.

The emergence of IoT technology has presented a transformative solution to address these challenges. By enabling ubiquitous connectivity and real-time data exchange, IoT has empowered the evolution of electrical grids into smarter, more efficient infrastructures capable of continuous monitoring, analysis, and optimization.

The key roles of IoT in modern power grids include:

Real-Time Monitoring and Data Analytics: IoT-enabled sensors and devices collect vast amounts of data from across the grid, providing utility providers with granular insights into energy consumption patterns, system performance, and potential areas for improvement.
Intelligent Control and Automation: IoT-integrated systems can autonomously adjust grid operations, optimize power distribution, and respond to changing demand or supply conditions, enhancing overall efficiency and reliability.
Renewable Energy Integration: IoT facilitates the seamless integration of renewable energy sources, such as Solar PV and Wind Generating Systems, by enabling real-time monitoring, forecasting, and coordinated control of these distributed energy resources.
Enhanced Customer Engagement: IoT-enabled smart meters and connected devices empower consumers to actively monitor and manage their energy usage, fostering a more collaborative and responsive energy ecosystem.

However, the widespread adoption of IoT in power grids is not without its challenges. Scalability, energy efficiency, and cybersecurity concerns must be addressed to fully realize the potential of this transformative technology.

Proposed IoT-Based Smart Grid Monitoring and Control System

To tackle the challenges faced by modern power grids, this research introduces an innovative IoT-based Smart Grid energy surveillance system that harnesses the power of the Adaptive Neuro-Fuzzy Inference System (ANFIS). By combining the strengths of Artificial Neural Networks (ANNs) and Fuzzy Logic Systems, the proposed framework enhances power distribution and control, optimizing the integration of renewable energy sources into the grid.

The core components of the system include:

Hybrid Renewable Energy System: The proposed system integrates both Solar PV and Wind Generating Systems to create a Hybrid Renewable Energy System (HRES) capable of meeting varying load demands.
ANFIS-Based Control: An ANFIS controller is employed to regulate the power flow within the smart grid, ensuring optimal and stable operation by adaptively adjusting to changing environmental conditions and load requirements.
Wireless Sensor Network (WSN): A WSN is deployed across the grid infrastructure to enable real-time data collection and analysis, providing the necessary inputs for the ANFIS controller’s decision-making process.
IoT-Enabled Monitoring: The system leverages IoT technologies, including wireless communication modules and cloud-based platforms, to facilitate continuous monitoring, data transmission, and remote access to grid performance metrics.

The key steps in the proposed approach are as follows:

Modeling and Integration of Hybrid Renewable Energy System: The system integrates both Solar PV and Wind Generating Systems, ensuring a diversified and reliable energy supply to meet varying load demands.
ANFIS-Based Power Flow Control: The ANFIS controller dynamically adjusts the power distribution between the renewable energy sources and the grid, optimizing the system’s performance and stability.
Wireless Sensor Network for Real-Time Monitoring: The WSN deployed across the grid collects and transmits critical data, such as voltage, current, and power metrics, to the ANFIS controller for real-time decision-making and optimization.
IoT-Enabled Communication and Monitoring: The system leverages IoT technologies, including wireless communication modules and cloud-based platforms, to facilitate seamless data exchange, remote access, and comprehensive monitoring of the grid’s performance.

By seamlessly integrating these components, the proposed IoT-based Smart Grid energy surveillance system enhances power quality, optimizes energy management, and provides substantial benefits in terms of efficiency and cost savings for both utility providers and consumers.

Modeling and Integration of Hybrid Renewable Energy System

The proposed system features a Hybrid Renewable Energy System (HRES) that combines both Solar PV and Wind Generating Systems to meet varying load demands. This approach ensures a diversified and reliable energy supply, mitigating the intermittency and fluctuations associated with individual renewable energy sources.

Solar Photovoltaic (PV) System Modeling

The solar PV system is modeled using a conventional approach, where the photovoltaic current (IPV) is determined based on the cell temperature, solar irradiation, and other relevant parameters. The relationship between the photocurrent and these factors is expressed by the following equations:

IPV = Iph – Io (e(EC VD Kf TA) – 1) – VD/Rp (1)

Iph = (τSC (TA – Tref) + ISC) GS (2)

These equations capture the nonlinear behavior of the solar PV module, enabling the system to accurately model its performance under different environmental conditions.

Wind Generating System (WGS) Modeling

The operational behavior of the Wind Generating System is determined by the characteristics of the wind turbine and the specific types of wind generators employed. The wind power is mathematically calculated using the following equation:

PW = CP (v, φ) ρ A² Vw³ (3)

Where, PW represents the power output, CP is the aerodynamic performance coefficient, ρ is the air-density factor, A is the swept area of the turbine, and Vw is the wind velocity.

The wind turbine’s performance is further described by the following equations:

CP (λ, β) = C1 (C2 λi – C3 β – C4) e-C5 λi + C6 λ (4)

λ = ω R / Vω (5)

1/λi = 1/λ + 0.081/β – 0.0352/β³ + 1 (6)

These equations capture the relationship between the wind speed, turbine characteristics, and the resulting power output, enabling the accurate modeling of the Wind Generating System.

Integration of Solar PV and Wind Generating Systems

The modeled Solar PV and Wind Generating Systems are integrated into a shared DC bus via power electronic converters, namely an AC/DC converter and a DC/DC converter, respectively. This configuration allows for the seamless combination of power generated from both renewable energy sources, providing a robust and reliable energy supply to the smart grid.

Adaptive Neuro-Fuzzy Inference System (ANFIS) for Power Flow Control

The proposed system leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to regulate the power flow within the smart grid, ensuring optimal and stable operation. ANFIS is a hybrid approach that combines the strengths of Artificial Neural Networks (ANNs) and Fuzzy Logic Systems, enabling it to effectively model complex systems and relationships.

ANFIS Architecture and Functionality

The ANFIS architecture consists of five main functional layers, each responsible for processing the input data and generating the corresponding output. The layers include:

Input Layer: Receives the input data, such as the power generated by the Solar PV and Wind Generating Systems, as well as the current load demand.
Fuzzification Layer: Converts the crisp input values into fuzzy membership degrees using appropriate membership functions.
Rule Layer: Generates fuzzy logic rules based on the fuzzified inputs, following an “if-then” structure.
Defuzzification Layer: Aggregates the results from the rule layer and applies defuzzification methods to generate a crisp output value.
Output Layer: Provides the final output, which in this case represents the reference power to be supplied by the renewable energy sources.

The ANFIS system is trained using historical data on power generation and load demand, allowing it to learn the underlying relationships and establish appropriate fuzzy rules. This training process enables the ANFIS controller to adaptively adjust its decision-making based on real-time inputs, optimizing the power flow and ensuring the stable operation of the smart grid.

ANFIS-Based Power Flow Control

The ANFIS controller regulates the power flow between the renewable energy sources (Solar PV and Wind Generating Systems) and the grid, based on the current load demand and available power generation. The controller aims to maximize the utilization of renewable energy while maintaining grid stability and reliability.

The reference power (Pr(t)) is calculated using the following equations:

Ps(t) = PG(t) (7)

PG(t) = PPV(t) + PW(t) (8)

Where, PG(t) represents the total power generated, PPV(t) is the power from the Solar PV system, and PW(t) is the power from the Wind Generating System.

The ANFIS controller dynamically adjusts the power distribution between the renewable energy sources and the grid, ensuring that the reference power Pr(t) is met while optimizing overall system performance and efficiency.

IoT-Enabled Monitoring and Communication

The proposed system incorporates IoT technologies to enable real-time monitoring, data analysis, and remote access to the smart grid’s performance metrics. This integration is achieved through the deployment of a Wireless Sensor Network (WSN) across the grid infrastructure.

Wireless Sensor Network (WSN) for Real-Time Monitoring

The WSN consists of a network of interconnected sensor nodes that collect and transmit critical data, such as voltage, current, and power measurements, from various points within the grid. This real-time data is then processed and analyzed to provide the ANFIS controller with the necessary inputs for its decision-making process.

The WSN serves as the “sensory organs” of the smart grid, gathering and transmitting data to facilitate efficient power distribution and control. The collected data is also utilized for comprehensive monitoring, allowing utility providers and consumers to track energy usage, detect anomalies, and optimize grid operations.

IoT-Enabled Communication and Remote Access

The integration of IoT technologies, including wireless communication modules and cloud-based platforms, enables seamless data exchange and remote access to the smart grid’s performance metrics. This connectivity allows utility providers to monitor the grid’s status, analyze consumption patterns, and make informed decisions to enhance overall efficiency and reliability.

Furthermore, the IoT-enabled system provides consumers with direct access to their energy usage data, empowering them to actively manage their consumption and participate in demand response programs. This enhanced transparency and user engagement foster a more collaborative and responsive energy ecosystem.

Performance Evaluation and Simulation Results

To assess the efficacy of the proposed IoT-based Smart Grid energy surveillance system, comprehensive simulations were conducted using MATLAB/Simulink and Proteus software. These simulations evaluated the performance of the system under various operating conditions, focusing on the following key aspects:

Solar PV System Performance: The simulation compared the power output characteristics of a solar PV module controlled by a conventional Proportional-Integral (PI) controller versus an ANFIS controller. The results demonstrated a significant improvement in power output, with the ANFIS controller achieving a 20.50% higher peak power compared to the PI controller.
Wind Generating System (WGS) Performance: Similar comparisons were made for the WGS, analyzing the power output under PI and ANFIS control strategies. The ANFIS controller exhibited superior performance, efficiently capturing and utilizing wind energy to meet the varying load demands.
Integrated Grid Monitoring and Control: The Proteus simulation showcased the real-time monitoring capabilities of the system, including the display of grid voltage, current, active power, reactive power, and apparent power. Additionally, the integration of IoT features, such as Wi-Fi connectivity and GSM-based SMS alerts, highlighted the system’s ability to provide comprehensive grid monitoring and user engagement.

The simulation results clearly demonstrate the advantages of the proposed IoT-based Smart Grid energy surveillance system. The integration of ANFIS control strategies, coupled with the real-time monitoring and communication capabilities enabled by IoT technologies, led to significant improvements in power quality, energy efficiency, and overall system performance.

Conclusion and Future Directions

This research introduces an innovative IoT-based Smart Grid energy surveillance system that addresses the challenges faced by modern power grids. By integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Wireless Sensor Network (WSN), the proposed framework enhances power distribution, control, and real-time monitoring, optimizing the integration of renewable energy sources into the grid.

The key contributions of this work include:

Development of a hybrid renewable energy system that combines solar PV and wind power generation to meet varying load demands.
Implementation of an ANFIS controller to regulate power flow within the smart grid, ensuring optimal and stable operation.
Deployment of a WSN to collect real-time data from various grid components, providing the necessary inputs for the ANFIS controller’s decision-making process.
Integration of IoT technologies, including wireless communication modules and cloud-based platforms, to enable comprehensive monitoring, data analysis, and remote access to grid performance metrics.

The simulation results demonstrate the superior performance of the ANFIS controller compared to traditional Proportional-Integral (PI) controllers, particularly in terms of power output optimization and grid stability. The incorporation of IoT features further enhances the system’s capabilities, allowing for seamless data exchange, remote access, and user engagement.

Moving forward, potential research directions for advancing IoT-based smart grid systems include:

Exploration of machine learning and artificial intelligence techniques for predictive maintenance, fault detection, and real-time optimization of renewable energy systems.
Integration of energy storage technologies, such as batteries or advanced capacitors, to better manage fluctuating power output and enable energy storage for periods of low renewable energy generation.
Development of standardized communication protocols and interoperability solutions to ensure seamless integration of IoT devices and renewable energy sources into the grid infrastructure.
Investigation of the scalability and robustness of intelligent control strategies, such as ANFIS, in large-scale power systems and their impact on grid stability and efficiency.

By addressing these research avenues, the integration of IoT and advanced control techniques can further enhance the reliability, sustainability, and cost-effectiveness of modern power grids, ultimately paving the way for a more intelligent and efficient energy future.