The extensive use of renewable energy sources (RESs) in energy sectors plays a vital role in meeting the present energy demand. The widespread utilization of allocated resources leads to multiple usages of converters for synchronization with the power grid, introducing poor power quality. The integration of distributed energy resources produces uncertainties which are reflected in the distribution system. The major power quality problems such as voltage sag/swell, voltage unbalancing, poor power factor, harmonics distortion (THD), and power transients appear during the transition of micro-grids (MGs). In this research, a single micro-grid is designed with PVs, wind generators, and fuel cells as distributed energy resources (DERs). A nonlinear auto regressive exogenous input neural network (NARX-NN) controller has been investigated in this micro-grid in order to maintain the above power quality issues within the specific standard range (IEEE/IEC standards). The performance of the NARX-NN controller is compared with PID and fuzzy-PID controllers. The single micro-grid is extended to design a three-phase large-scale realistic micro-grid structure to test the feasibility of the proposed controller. The realistic micro-grid is verified through addition of line-impedance, communication delay, demand response, and off-nominal situations. The proposed controller is also validated by simulating different test scenarios using MATLAB/Simulink and TMS320-based processor-in-loop (PIL) for real-time implementation.

Considering the advantage of the ability of data-mining techniques (DMTs) to detect and classify patterns, this paper explores their applicability for the protection of voltage source converter-based high voltage direct current (VSC-HVDC) transmission systems. In spite of the location of fault occurring points such as external/internal, rectifier-substation/inverter-substation, and positive/negative pole of the DC line, the stated approach is capable of accurate fault detection, classification, and location. Initially, the local voltage and current measurements at one end of the HVDC system are used in this work to extract the feature vector. Once the feature vector is retrieved, the DMTs are trained and tested to identify the fault types (internal DC faults, external AC faults, and external DC faults) and fault location in the particular feeder. In the data-mining framework, several state-of-the-art machine learning (ML) models along with one advanced deep learning (DL) model are used for training and testing. The proposed VSC-HVDC relaying system is comprehensively tested on a symmetric-monopolar-multi-terminal VSC-HVDC system and presents heartening results in diverse operating conditions. The results show that the studied deep belief network (DBN) based DL model performs better compared with other ML models in both fault classification and location. The accuracy of fault classification of the DBN is found to be 98.9% in the noiseless condition and 91.8% in the 20 dB noisy condition. Similarly, the DBN-based DMT is found to be effective in fault locations in the HVDC system with a smaller percentage of errors as MSE: 2.116, RMSE: 1.4531, and MAPE: 2.7047. This approach can be used as an effective low-cost relaying support tool for the VSC-HVDC system, as it does not necessitate a communication channel.

This paper presents a comparative study of robust nonlinear controller based on sliding mode control theory and internal model controller-based PI for VSC-HVDC transmission system. A modified sliding mode controller or 'SAT' is used to handle the uncertainties and enhance the system performance, under various disturbance conditions. The new sliding mode control compensates the influence of dynamics and chattering phenomena on the system response. It also improves the robustness of the reaching phase of the modified sliding mode control. The effectiveness of the proposed controllers is demonstrated through digital simulation studies using MATLAB/SIMULINK software package. The simulation results demonstrate that the proposed controller contribute appreciably towards improving the damping behaviour of the VSC-HVDC system under various disturbances compare to conventional PI and IMC-based PI controller.

High voltage DC (HVDC) transmission have controllable quality with potential for towards power system stability. This paper presents the methodology to analyze the stability and robustness characteristicsof VSC-HVDC controls in a multi machine power system over several operating conditions. The dynamics of VSC-HVDC is formulated using synchronous reference.i.e., d-q axis model. Fuzzy logic and sliding mode controller are used to cancel nonlinearity and offers invariant stability to model uncertainties pertaining due to various disturbances in the system such as fault at converter bus, change in the short circuit ratio and voltage reduction at DC bus. The efficiency of the proposed sliding mode controller is validated via simulation tool using MATLAB/SIMULINK. Further the efficiency of robust controller may be enhanced by selecting the optimized controller parameters. The simulation results revealed that the optimized sliding mode controller helped in a great deal in getting better stability of the VSC-HVDC system with respect to parameter variations and exterior load turbulence compared to conventional PI and fuzzy controller under various fault conditions.