To combat cyber threats in the smart grid, an intrusion detection system can be integrated into the advanced metering infrastructure. Anomaly-based intrusion detection can detect even the tiniest changes in the parameter under investigation, whereas signature-based intrusion detection only recognises known attacks. The growing usage of smart grids necessitates the classification, identification, and implementation of countermeasures to threats. At the absolute least, smart grids must be protected against cyberattacks; thus, the highest level of information security must be offered. As a result of digitisation and the usage of more smart applications, the research looked at a variety of attack types, smart grid assaults, and major cyber threats on the voltage regulation. Machine learning techniques that analyse data in real time and formulate patterns to recognise an attack and scan through huge data for anomalies can be implemented into the advanced metering infrastructure (AMI) for intrusion detection for anomaly-based intrusion detection. The comparative test study done for the research found that the proposed method, median absolute deviation for anomaly identification in smart metering datasets, produced the most accurate and precise differentiations with the highest accuracy and precision. The median absolute deviation (MAD) algorithm model is trained using test data, and raw predictions are made, before true data are used to derive final test result parameters, precision, recall, and F1 scores. The methodology of the entire study is discussed in this paper, as well as how the MAD algorithm is best suited for anomaly-based intrusion detection, as well as result comparisons of other machine learning algorithms.

This paper presents the design and implementation of proportional integral derivative (PID) structured centralised and decentralised automatic generation control (AGC) controllers in more realistic 2-area interconnected power system with multi sources power plants in each area. The standard search technique; genetic algorithms (GAs) is adapted to search the optimal parameters of centralized and decentralized AGC controllers by developing integral square error (ISE) performance index as an objective function. However, the systems dynamic responses are obtained to compare performance of these AGC controllers for 1% step load disturbance at respective control areas in power systems. It has been investigated that when centralized AGC scheme is implemented for mitigating load disturbance in power system, the disturbed area is responsible to generate the extra power to cope with local load disturbance. Besides, it has been also observed that when decentralized AGC scheme was implemented in power systems, both the areas area responsible to remove load disturbance by contributing generation, which is equal to load disturbance.

Wind and solar photovoltaic (PV) based hybrid renewable energy generation are environmentally friendly and reasonable. This study describes a hybrid distributed energy generations (DEGs)-based microgrid framework where DC-DC converter, three-stage inverter with fuzzy logics control (FLC), and LC filter channel are coupled with the PV and wind energy. In India, wind and PV energies are affordable for hybrid power frameworks since they are ecological well-disposed and broadly accessible. Generally, these sources produce a fluctuating yield voltage that prompts harm to the working grid on a steady inventory. The proposed model of the hybrid-based framework is executed utilizing MATLAB/Simulink. A boost converter being associated with PV cluster is associated with the basic DC bus also a battery is used by a two-way DC to DC converter, and connected into the utility framework by a typical DC to AC inverter. Wind cluster and PV is linked with maximum power point tracking (MPPT) to produce the higher capacity to the grid, and the charging and discharging of the battery energy can be done to adjust the energy between DEGs generation and utility network. In this paper, various cases of harmonic analysis are executed on MATLAB-simulation and feasibility of proposed grid models and FLC-based intelligent control.

To solve the difficulty in selecting the crossover probability and mutation probability in genetic algorithms, a fuzzy immune algorithm based on adaptive estimation of crossover probability and mutation probability in a fuzzy reasoning system is proposed, and it is used in the parameter optimization design of a two‐degree‐of‐freedom PID controller. According to the experiment and simulation results, classic genetic algorithm evolution tends to halt after 37 generations, with a fitness value of 7.135, whereas fuzzy genetic algorithm evolution tends to stop after 20 generations, with a fitness value of 7.486. The 2‐DOF PID controller that was created can give the system strong target value following and interference suppression features at the same time.