This paper evaluates the performance of six different machine learning (ML) algorithms for classifying power quality disturbances (PQDs), with statistical features extracted using discrete wavelet transform (DWT) as feature input. The statistical features have been extracted from coefficients of multi-resolution analysis (MRA) using four different mother wavelets: Daubechies 4 (‘db4’), ‘haar’, Discrete Meyer (‘dmey’), Coiflets 4 (‘coif4’). The performance analysis has been carried out with 5,500 synthetic signals pertaining to eleven different PQDs generated in accordance with IEEE 1159-2019. Moreover, the performance of the classifiers trained with synthetic signals has been investigated under the influence of unseen noisy signals, hardware PQD signals obtained from the experimental setup, and real PQD events. The analysis indicates that the performance of the extra tree (ET) classifier with the features extracted using ‘haar’ as a mother wavelet is superior and robust in comparison to other classifiers, viz k-nearest neighbor (kNN), random forest (RF), decision tree (DT), logistic regression model (LRM), and gaussian naïve bayes (GNB) with features extracted using different mother wavelets. Furthermore, the ‘haar-ET’ based technique demonstrated remarkable performance in classifying PQDs, showing strong generalization to both unseen hardware and noisy signals, and achieving consistent results when tested with real PQD events.

The global boom in the importance of renewable energy (RE) resources has accelerated the transition from traditional energy sources to sustainable alternatives. Selecting an optimum RE power plant is a complex task influenced by geographical factors, climatic differences, and regional peculiarities. Furthermore, incorporating numerous conflicting criteria, such as technical, economic, environmental, and social variables, complicates the decision-making process, rendering it a multi-criteria decision making (MCDM) problem. This research aims to solve the current complicated decision-making process by utilizing the CRITIC integrated EDAS-CODAS-CoCoSo MCDM framework, specifically focusing on selecting the most suitable RE power plant to be developed in the Indian environment. Within the MCDM system, a case study was analyzed using five unique RE sources, namely geothermal, solar, wind, hydro, and biomass. The evaluation was based on six competing elements classified into three broad categories: mechanical features, economic considerations, and environmental implications. The CRiteria Importance Through Intercriteria Correlation method (CRITIC) was initially used to determine the objective criteria weights, which were subsequently integrated into the Evaluation based on Distance from Average Solution (EDAS), COmbinative Distance-based ASsessment (CODAS), and Combined Compromise Solution (CoCoSo) methodologies to determine the preference ranking order of the alternatives. The data indicate that hydroelectric power plants are the most ideal alternative, followed by solar electricity, while geothermal and biomass are the least favorable among the considered RE sources. This study analyzes the rankings of the three deployed MCDM models and checks their stability using a sensitivity analysis. Another feature of the final results shows that EDAS and CoCoSo perform slightly better than CODAS in terms of ranking efficiency. The in-depth study of these findings provides crucial insights and direction for policymakers and stakeholders in India’s renewable energy sector. Clinical trial number: Not applicable.

The Direct Power Control strategy has become popular as an alternative to the conventional vector oriented control strategy for grid connected PWM converters. In this paper, Direct Power Control as applied to various applications of grid connected converters is reviewed. The Direct Power Control for PWM rectifiers, Grid Connected DC/AC inverters applications such as renewable energy sources interface, Active Power Filters, Doubly Fed Induction Generators and AC-DC-AC converters are discussed. Control strategies such as Look-Up table based control, predictive control, Virtual Flux DPC, Model based DPC and DPC-Space Vector Modulation are critically reviewed. The effects of various key parameters such as selection of switching vector, sampling time, hysteresis band and grid interfacing on performance of direct power controlled converters are presented.

The Unified Power Quality Conditioner (UPQC) is a promising solution for mitigating multiple Power Quality(PQ) issues in distribution systems, including harmonics, poor power factor, voltage sag/swell and voltage imbalance. The conventional Sliding Mode Controller (SMC) in UPQCs suffers from wide switching frequency variations, chattering problems, and inherent active and reactive power coupling. This study proposes a nonlinear control method, Sliding Mode-based Direct Power Control (SMC-DPC), for the simultaneous regulation of the shunt and series compensators in a UPQC. By optimizing voltage vector selection based on real-time power errors, the proposed method effectively mitigates chattering, and reduces switching frequency variations, and ensures precise tracking of instantaneous active and reactive powers even in the presence of coupling effects. The proposed approach simplifies the system design and improves steady state and dynamic power tracking. The simulation results in MATLAB Simulink® on a 20 kVA system demonstrate that the proposed method achieves a source current THD of 1.52%, compared to 2.31% for conventional SMC and 5.11% for linear controller. Furthermore, the method is robust to grid parameter variations and demonstrates satisfactory performance in both strong and weak grids. In weak grids, the proposed method reduces the line losses by 13.71% and 14.54% compared to SMC and linear controller respectively. The reported results comply with international standards such as IEEE-519 and IEC 61000-3-12, confirming effectiveness of SMC-DPC for enhancing PQ in distribution system.