This paper presents a comparative analysis of the intelligent controllers for temperature control of a benchmark thermal system. The performances of the proposed wavelet-based multiresolution proportional-integral derivative (PID) (MRPID) controller, which can also be stated as a multiresolution wavelet controller, are compared with the conventional PID controller and the adaptive neural-network (NN) controller. In the proposed MRPID temperature controller, the temperature error of actual and command temperatures of a thermal system is decomposed into different frequency components at various scales of the discrete wavelet transform (DWT). The wavelet-transformed coefficients of temperature error at different scales of the DWT are scaled by their respective gains and then are added together to generate the control signal for the thermal system. The performances of these intelligent controllers are investigated in both simulation and experiments for different operating conditions of the thermal system. The performances of the wavelet-based MRPID controller are found superior to the conventional PID and adaptive NN controllers for temperature control of the thermal systems.

Low-power photovoltaic distributed generators are increasing rapidly in number by the grids of the modern era, but they also bring up the concern of grid stability. To maintain grid stability, it is essential for the network operators to update the grid codes at regular intervals due to the case of highly penetrated grid-connected photovoltaic systems (GCPVs). Integration of large-scale electrical grids with renewable energy sources, one being photovoltaic systems, faces the challenge of riding through low-voltage (LVRT) phases. As compared to the previous grid codes for power generation, recent advances require distributed generation resources to provide for such capabilities under grid faults. This work contributes to the ongoing investigation of this specified and destabilizing fault condition. Various simulations of a PV microgrid system are carried out with the ability to ride through low-voltage faults, with the help of a DC-chopper circuit to absorb DC-link over-voltage, and the current is also maintained within acceptable limits according to the required standards. The fundamental contribution of this research is a proposed neural network (NN) control framework. This framework effectively detects voltage sags, comprehends their characteristics, and provides support to the system by injecting reactive current in accordance with the demands imposed by designated grid codes. This NN control model has been systematically developed utilizing data gathered from a vast array of testing simulations conducted under different and dynamic conditions. The proposed strategy, on the other hand, is compared with the RMS fault detection method in combination with the conventional LVRT algorithm. The NN control approach showed better results as compared to the conventional methods in terms of accuracy and robustness, especially when confronted with difficult sag situations.

This paper presents the practical implementation of a new algorithm for the detection and diagnosis of stator electrical faults in three-phase interior permanent magnet (IPM) motors. Motor current signature analysis is performed off-line on the collected data to extract the most significant features from the fault currents. It has been found that magnitudes of harmonics for motor fault currents can be used to discriminate between different faulted and normal unfaulted conditions. The proposed algorithm utilized the multiresolution property of the WPT, and is based on the identification of fault harmonics using the selected mother wavelet `db3'. The novel wavelet packet transform (WPT) based algorithm for the protection of IPM motors is implemented and tested on-line on the laboratory 1-hp and 5-hp IPM motors. The single phasing fault, single line to ground (L-G) fault and line-to-line (L-L) fault are investigated. In all the tests carried out, the faults are detected, and the trip signal is initiated almost at the instant or within one cycle of the fault occurrence

Enhancing the performance of maximum power point tracking (MPPT) methods is essential for optimizing the operation of solar systems under any weather condition. This improvement is crucial because MPPT methods serve as the controllers for power converters, directly influencing the ability to maintain a steady and efficient energy output from solar installations. Therefore, we propose a deep symbolic regression (DSR)-based MPPT method in standalone direct current microgrids, which is particularly useful under drastically shifted meteorological conditions. This study aimed to improve the significant performance parameters of the MPPT approach, such as the rate of convergence during transient periods, tracking accuracy, steady-state stability, and overshoot/undershoot reduction, creating more reliable and efficient solar microgrid systems. To evaluate the proposed method, performance comparisons were made with those of existing advanced algorithms, namely particle swarm optimization-, artificial neural network-, and adaptive neuro-fuzzy inference system-based MPPT methods. The comparative results demonstrate a significant improvement with the proposed method. Moreover, the simple mathematical expression obtained by the DSR process minimizes the computational complexity of the control.