## 7 Recently Proposed Intelligent Techniques for Power Systems  
In order to solve the optimal power flow (OPF) problem, a unique method based on a modified sine-cosine technique was proposed [128]. The modified sine-cosine algorithm (MSCA) seeks to shorten calculation time while improving the practicality and ability to identify the best answer. The OPF problem is solved for several benchmark test systems to validate the MSCA. To demonstrate the efficacy and potential of the sine-cosine algorithm (SCA) and MSCA algorithms, the proposed MSCA is contrasted with alternative optimization techniques. Ref. [128] presented a novel hybrid algorithm that combines atom search optimization with simulated annealing algorithms. A power system stabilizer used in a single-machine infinite-bus power system was proposed to be optimized using the newly created enhanced algorithm, the improved atom search optimization algorithm. The assessments were initially carried out by comparing the outcomes with those of the genetic algorithm, the simulated annealing technique, particle swarm optimization, the gravitational search algorithm, and the original iteration of the atom search optimization algorithm. The outcomes demonstrated the created hybrid algorithm's significant potential in striking a balance between the exploration and exploitation stages. The suggested approach outperformed other recently reported top-performing power system stabilizer design algorithms. [PERSON] et al. [129] introduce a multi-criteria decision-making strategy based on interval type-2 fuzzy sets for choosing the ideal site for electric charging stations. Simulated annealing, which combines two separate aggregation operators--linguistic weighted sum and average--with the interval type-2 membership function parameters, improves this approach. An actual issue with public transportation faced by the municipal bus business in Istanbul is addressed using the suggested overall reusable multi-stage solution strategy. The outcomes show that the method does enhance the model by better capturing the associated uncertainties embedded in the interval type-2 membership functions, creating a fuzzy system with greater efficacy. These findings are supported by experts who also found that the SA-improved interval type-2 fuzzy approach produces more trustworthy outcomes when choosing the optimal locations for electric bus charging stations. [PERSON]. [130] suggested a particle swarm optimization (PSO)-based tuning methodology for power system stabilizers (PSSs) that is effective for systems with 10 or even more units. The commercial simulation program Dig Silent Power Factory's source language was used to build our novel methodology. The methodology was used on various test systems, demonstrating the efficacy and potential of the suggested method. In order to ensure the safe and reliable operation of a power system, [PERSON]. [131] attempted to resolve the problem of anomaly detection. The one-class support vector machine (OCSVM), which is appropriate for classifying imbalanced data, is used, since the fraction of aberrant data in the operation of the power system is relatively low. OCSVM's performance depends on the given parameters, so making the wrong decision will reduce its classification precision and generalizability. The author optimized the parameters of the OCSVM using PSO. The original PSO method readily reaches the local optimum and converges slowly. This problem is addressed by the author's modified PSO method for parameter optimization, which introduces adaptive population splitting and adaptive speed weighting to speed up convergence and aid in algorithmic escape from the local optimal location. The efficiency of the suggested approach is demonstrated in tests using natural power system experimental data sets and standard benchmarks. One more technique is ant colony algorithms, which are inspired by the actions of natural ant colonies in working together to solve problems by changing the environment and utilizing indirect communication.