A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Frequency Control of Microgrid Network using Intelligent Techniques – ANN, PSO and ANFIS

The electric grid is a complex system that transmits electricity from the point of generation to the point of consumption. According to the IEA, worldwide energy-related carbon emissions in 2021 will be 36.3Gt, 60% greater than at the start of the industrial revolution. Researchers have used intelligent solutions for power system frequency regulation to ensure that the system\u27s frequency is maintained. A proper frequency control of the microgrid necessitates the modeling and study of the systems. To emulate the operation of the human brain, frequency control employs a variety of artificial intelligence-based computer algorithms. This thesis generates a complete state space model of a microgrid composed of solar power plants, wind turbines, battery storage systems, and backup generators. The system frequency control was created for this system and analyzed against a benchmark PID controller utilizing several intelligent controllers such as PSO optimized PID, ANN, and ANFIS. The suggested intelligent frequency controllers were be simulated and validated using MATLAB/ Simulink

Review of dynamic positioning control in maritime microgrid systems

For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper

Comparison between unipolar and bipolar single phase grid-connected inverters for PV applications

An inverter is essential for the interfacing of photovoltaic panels with the AC network. There are many possible inverter topologies and inverter switching schemes and each one will have its own relative advantages and disadvantages. Efficiency and output current distortion are two important factors governing the choice of inverter system. In this paper, it is argued that current controlled inverters offer significant advantages from the point of view of minimisation of current distortion. Two inverter switching strategies are explored in detail. These are the unipolar current controlled inverter and the bipolar current controlled inverter. With respect to low frequency distortion, previously published works provide theoretical arguments in favour of bipolar switching. On the other hand it has also been argued that the unipolar switched inverter offers reduced switching losses and generates less EMI. On efficiency grounds, it appears that the unipolar switched inverter has an advantage. However, experimental results presented in this paper show that the level of low frequency current distortion in the unipolar switched inverter is such that it can only comply with Australian Standard 4777.2 above a minimum output current. On the other hand it is shown that at the same current levels bipolar switching results in reduced low frequency harmonics

Comparison between unipolar and bipolar single phase grid-connected inverters for PV applications

An inverter is essential for the interfacing of photovoltaic panels with the AC network. There are many possible inverter topologies and inverter switching schemes and each one will have its own relative advantages and disadvantages. Efficiency and output current distortion are two important factors governing the choice of inverter system. In this paper, it is argued that current controlled inverters offer significant advantages from the point of view of minimisation of current distortion. Two inverter switching strategies are explored in detail. These are the unipolar current controlled inverter and the bipolar current controlled inverter. With respect to low frequency distortion, previously published works provide theoretical arguments in favour of bipolar switching. On the other hand it has also been argued that the unipolar switched inverter offers reduced switching losses and generates less EMI. On efficiency grounds, it appears that the unipolar switched inverter has an advantage. However, experimental results presented in this paper show that the level of low frequency current distortion in the unipolar switched inverter is such that it can only comply with Australian Standard 4777.2 above a minimum output current. On the other hand it is shown that at the same current levels bipolar switching results in reduced low frequency harmonics

Review of soft computing models in design and control of rotating electrical machines

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines

Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

IMPROVEMENT OF POWER QUALITY OF HYBRID GRID BY NON-LINEAR CONTROLLED DEVICE CONSIDERING TIME DELAYS AND CYBER-ATTACKS

Power Quality is defined as the ability of electrical grid to supply a clean and stable power supply. Steady-state disturbances such as harmonics, faults, voltage sags and swells, etc., deteriorate the power quality of the grid. To ensure constant voltage and frequency to consumers, power quality should be improved and maintained at a desired level. Although several methods are available to improve the power quality in traditional power grids, significant challenges exist in modern power grids, such as non-linearity, time delay and cyber-attacks issues, which need to be considered and solved. This dissertation proposes novel control methods to address the mentioned challenges and thus to improve the power quality of modern hybrid grids.In hybrid grids, the first issue is faults occurring at different points in the system. To overcome this issue, this dissertation proposes non-linear controlled methods like the Fuzzy Logic controlled Thyristor Switched Capacitor (TSC), Adaptive Neuro Fuzzy Inference System (ANFIS) controlled TSC, and Static Non-Linear controlled TSC. The next issue is the time delay introduced in the network due to its complexities and various computations required. This dissertation proposes two new methods such as the Fuzzy Logic Controller and Modified Predictor to minimize adverse effects of time delays on the power quality enhancement. The last and major issue is the cyber-security aspect of the hybrid grid. This research analyzes the effects of cyber-attacks on various components such as the Energy Storage System (ESS), the automatic voltage regulator (AVR) of the synchronous generator, the grid side converter (GSC) of the wind generator, and the voltage source converter (VSC) of Photovoltaic (PV) system, located in a hybrid power grid. Also, this dissertation proposes two new techniques such as a Non-Linear (NL) controller and a Proportional-Integral (PI) controller for mitigating the adverse effects of cyber-attacks on the mentioned devices, and a new detection and mitigation technique based on the voltage threshold for the Supercapacitor Energy System (SES). Simulation results obtained through the MATLAB/Simulink software show the effectiveness of the proposed new control methods for power quality improvement. Also, the proposed methods perform better than conventional methods

Microgrid Energy Management

In IEEE Standards, a Microgrid is defined as a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries, which acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes. This Special Issue focuses on innovative strategies for the management of the Microgrids and, in response to the call for papers, six high-quality papers were accepted for publication. Consistent with the instructions in the call for papers and with the feedback received from the reviewers, four papers dealt with different types of supervisory energy management systems of Microgrids (i.e., adaptive neuro-fuzzy wavelet-based controls, cost-efficient power-sharing techniques, and two-level hierarchical energy management systems); the proposed energy management systems are of quite general purpose and aim to reduce energy usages and monetary costs. In the last two papers, the authors concentrate their research efforts on the management of specific cases, i.e., Microgrids with electric vehicle charging stations and for all-electric ships