This paper shows the health monitoring and assessment of a three‐phase induction motor in abnormal conditions using a machine learning algorithm. The convolutional neural network (CNN) and recurrent neural network (RNN) algorithms are the prominent methods used in machine learning algorithms, and the combined method is known as the CRNN method. The abnormal conditions of a three phase‐induction motor are represented by three‐phase faults, line‐to‐ground faults, etc. The pattern of fault current is traced, and key features are extracted by the CRNN algorithm. The performance parameters like THD (%), accuracy, and reliability of abnormal conditions are measured with the CRNN algorithm. The assessment of abnormal conditions is being realized at the terminals of a three‐phase induction motor. A fuzzy logic controller (FLC) is also used to assess such abnormalities. It is observed that performance parameters are found to be better with the CRNN method in comparison to CNN, RNN, ANN, and other methods. Such a realization makes the system more compatible with abnormality recognition.

The article presents the design and development of invasive weed optimization (IWO) for the sensor less speed control of doubly fed induction generator (DFIG) under balanced and unbalanced conditions. A healthy condition represents the balanced condition while unbalancing condition is characterized by unhealthy conditions like three-phase fault, single line to ground fault, the line-to-line fault, and double line to ground fault. The DFIG is driven by wind and integrated with the grid. The advantages associated with IWO technique are a simple mathematical approach and less data computation. Normally, DFIG consists of two back-to-back converters namely grid side converter (GSC) and rotor side converter (RSC). The GSC is an uncontrolled converter while the RSC is a controlled converter. The existing methods have poor performance parameters like settling time, peak overshoot for balanced conditions, and poor power quality parameters like total harmonic distortion (THD) for unbalanced conditions. An IWO technique has been applied to overcome such limitations. The effectiveness of the sensor less speed control is also analyzed with other techniques like Adaptive Neuro-Fuzzy Interference System (AN-FIS) & artificial neural network (ANN). The design of ANN is based on the feed-forward method using back propagation delay and the design of ANFIS is based on adaptive control and state space control strategy. It is observed that performance parameters like peak overshoot and settling time for the sensor less speed of DFIG are found to be more profound with IWO in comparison to ANFIS, ANN, and other existing techniques for balanced conditions. Similarly in the unbalanced condition, faulty current approaches are quite closer to their healthy state with the IWO method in comparison to other methods. In addition to this, minimum distortion (%THD) for the grid current under unbalanced conditions is also attained with IWO in comparison to ANFIS, ANN, and other existing techniques. Such application of IWO makes the system highly efficient and robust.

The integration of conventional sources with the grid has many challenges, like carbon emission, optimal cost of the system, and power quality issues. All these shortcomings create a non-sustainability in the environment, which is of great concern. In order to overcome such issues, a hybrid system is designed that is composed of various components or sources like wind energy, solar photovoltaic energy, thermal energy, and battery energy storage with the purpose of providing an environmentally friendly, economically viable, sustainable, and reliable solution. The objective is to reduce the carbon capture, carbon utilization, and carbon footprint. The carbon footprint is measured as the optimal difference between carbon capture (CC) and its utilization (CU), and carbon emission is represented as loss of carbon emission (LCE). Another objective is to reduce the optimal size of components, the distortion level, and the optimal cost in terms of loss of cost of energy (LCOE) for the various values of loss of power supply probability (LPSP). All the above objectives are accomplished by designing a nonlinear multi-objective problem. The designed nonlinear multi-objective function is based on a hybrid hysteresis fuzzy algorithm. The proposed algorithm is a combination of both fuzzy logic controllers and the hysteresis band method. The effectiveness of the proposed topology is tested on an IEEE standard 9 bus system. It is observed that a nonlinear hybrid hysteresis fuzzy algorithm provides a reliable and sustainable solution for optimal cost with a reduced effective carbon footprint and minimal distortion by maintaining the proper balance between carbon capturing and carbon utilization. The average values of LCOE,LCE, CU, and CC with the proposed method for various LPSP are found to be 0.5926 $/KWh, 70.64 g CO2/kWh, 89 g CO2/kWh, and 159 g CO2/kWh, which are the least among all methods.

The article presents the design and implementation of a hybrid fuzzy poisson fractional order proportional integral derivative controller (h-FL-PO-FPID controller) in the wind-driven permanent magnet synchronous generator (PMSG). The h-FL-PO-FPID controller is a hybrid controller that is formed by the combination of a fuzzy logic controller, a poisson distribution (PO), a fractional order proportional integral, and a derivative (FO-PID) controller. The integration of PMSG with the grid via rectifier and inverter has many challenges, like high peak overshoot, large settling time, more harmonics at the output end, and an inappropriate voltage profile. The total harmonic distortion (THD) is used to measure the harmonics. The PMSG is driven by a wind turbine. The rectifier & inverter controlling has been done with fuzzy logic controllers (FLC), FO-PID controllers, and proportional integral and derivative (PID) controllers. A closed-loop current control mechanism has been developed for controlling the rectifier and inverter with the given three methods. It is observed that peak overshoot (%) and settling time (sec) of the DC link voltage and rotor speed are found to be minimum with h-FL-PO-FPID in comparison to FLC, FO-PID, and PID existing methods under various loading conditions.In addition to this, an improved voltage profile is obtained with h-FL-PO-FPID in comparison to other methods, which means the least THD (%) of the three-phase voltage is attained with h-FL-PO-FPID with respect to other and existing methods under various loading conditions.

The several artificially intelligent techniques used in demand-side management (DSM) are exhaustively reviewed in this article. The objective of the demand-side management is to connect and disconnect the available generating units with the variable loads with the objective of meeting peak load and base load demand. The meeting of load demand with the adjustment in available generating units is accompanied by demand-side management. It is observed that ANN is utilized for short-term load and pricing forecasting, and other nature-encouraged optimization techniques like swarm intelligence, game theory, deep learning methods, etc. may be used as speculation methods because these optimization techniques are less precise. Demand-side management involves highly complicated losses in all existing methodologies that have been controlled and reduced by artificial intelligence and machine learning. Smart pricing for customers results from increasing the economic efficiency of consumption by promoting energy load demand during off-peak hours and discouraging energy load demand during peak hours. Less fuel consumption also helps to reduce carbon emissions from these power generation projects, which helps power suppliers save on additional fuel costs due to severe and unpredictable margin variations in power generation. The various aspects of the charging of electric vehicles using demand-side management, considering the clustering methods and forecasting strategies with brief descriptions, data, range have been assessed by using the different artificial intelligence and machine learning methods.

In this paper, performance analysis of power system network is carried out by injecting the inter-turn fault at the power transformer. The injection of inter-turn fault generates the inrush current in the network. The power system network consists of transformer, current transformer, potential transformer, circuit breaker, isolator, resistance, inductance, loads, and generating source. The fault detection and termination related to inrush current has some drawbacks and limitations such as slow convergence rate, less stability and more distortion with the existing methods. These drawbacks motivate the researchers to overcome the drawbacks with new proposed methods using wavelet transformation with sample data control and fuzzy logic controller. The wavelet transformation is used to diagnose the fault type but contribute lesser for fault termination; due to that, sample data of different signals are collected at different frequencies. Further, the analysis of collected sample data is assessed by using Z-transformation and fuzzy logic controller for fault termination. The stability, total harmonic distortion and convergence rate of collected sample data among all three methods (wavelet transformation, Z-transformation and fuzzy logic controller) are compared for fault termination by using linear regression analysis. The complete performance of fault diagnosis along with fault termination has been analyzed on Simulink. It is observed that after fault injection at power transformer, fault recovers faster under fuzzy logic controller in comparison with Z-transformation followed by wavelet transformation due to higher stability, less total harmonic distortion and faster convergence.