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.
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.
The Phasor Measurement Units (PMU) installations worldwide along with the advancements in computing and communications enabled the time tagged phasor measurements of voltages and currents. These real time phasor measurements have become the backbone of wide-area monitoring, protection, and control for various applications in power systems. This paper highlights the monitoring of voltages and phase angles across different nodes that provides very useful information indicating the system state and its proximity to stability limit. The most crucial task is placement of PMUs at minimum number of buses for complete observability. Recursive spanning tree algorithm of PSAT is applied to find out the minimal placement locations for observability of all buses. The Thevenin's equivalent parameters have been obtained from the measured and estimated voltages at the load buses and impedance matrix Zbus. The parameters obtained are used to find the voltage stability boundary. Results on IEEE-14 bus system and IEEE-30 bus system are presented to illustrate the proposed approach.
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