With the growing presence of renewable energy sources (RESs), the necessity for adaptive and robust control strategies becomes more pronounced. This article proposes a self‐adaptive bonobo optimizer (SABO)‐based proportional integral one plus double derivative (PI‐(1+DD)) controller that offers a novel solution to the load frequency control (LFC). It draws inspiration from the reproductive strategies of bonobos, employing unique mating behaviors to enhance optimization processes. This innovative approach introduces memory capabilities, repulsion‐based learning, and diverse‐mating strategies. It is developed to tune the PI‐(1+DD) controller for handling the LFC in a two‐area power system involving a thermal plant and RESs of a wind farm. The proposed SABO algorithm is applied in a comparative manner to the standard bonobo optimization algorithm (BOA), Coot algorithm, particle swarm optimizer (PSO), and Pelican optimization approach (POA). Also, the SABO‐based PI‐(1+DD) controller is contrasted to PI and PIDn controllers. The simulation findings distinguish the proposed SABO‐based PI‐(1+DD) controller as a versatile and adaptive controller offering a more resilient and efficient approach to tackle the complexities introduced by the evolving energy landscape. It demonstrates its potential to significantly improve the dynamic response of power systems, particularly in the face of step load changes and random fluctuations. The proposed SABO‐based PI‐(1+DD) controller shows significant enhancement compared to BOA, Coot, POA, and PSO with 38.81%, 46.27%, 16.79%, and 37.40%, respectively. Also, it demonstrates an impressive percentage improvement of 97.1% compared to the PIDn controller and 74.88% over the PI controller considering random consecutive fluctuations in the system.

Investigating and addressing fault detection is crucial for advancing the reliability, performance, and cost‐effectiveness of grid‐connected inverter systems, thereby contributing to the stability and efficiency of modern power grids. This study introduces a novel approach for detecting and classifying open‐circuit faults (OCFs) in three‐level neutral point clamped (3‐L‐NPC) inverters connected to the grid. The proposed algorithm swiftly identifies faulty switches and clamping diodes using distorted current signals and model predictive control (MPC), eliminating the need for additional hardware or complex computations. By addressing the challenge of identifying the specific switch under grid‐connected conditions, the proposed method achieves faster detection and identification of all switches and clamping diodes in less than one fundamental period which is very good compared with recent studies and considering that no extra sensors are used. Furthermore, this work demonstrates the efficacy of MPC in tolerating OCFs in clamping diodes, showcasing its potential to enhance system resilience and performance. The proposed strategy significantly improves the reliability of 3‐L‐NPC inverters by ensuring prompt and accurate fault detection and classification. Both experimental and simulation results confirm the efficacy of the suggested fault detection and identification approach, emphasizing its practical applicability in real‐world grid‐tied inverter systems.

This study proposes a deep neural network (DNN)–based regression model for predicting the excess air ratio, which is a critical indicator for optimizing combustion efficiency and minimizing harmful emissions in industrial combustion systems. The chemiluminescence signals of the OH∗ radicals and fuel pressure were used as the input features for the prediction model. To evaluate the effect of the multidimensional input, Case 1, with only the OH∗ radical signal as a single input, was compared with Case 2, with the OH∗ radical signal and fuel pressure as the inputs. The results showed that the Case 2 model reduced the mean absolute error (MAE), mean relative error (MRE), and root mean squared error (RMSE) by approximately 40.71%, 41.85%, and 19.69%, respectively, compared to Case 1, and the average relative prediction error rate was also 2.25% lower. These results demonstrate the potential for improving the accuracy and generalization ability of the model by incorporating multidimensional input features. Therefore, DNN models using multidimensional inputs can contribute to the design and implementation of combustion control systems to optimize the combustion efficiency and reduce harmful emissions in industrial combustion systems by predicting the excess air ratio.

The use of biodiesel is becoming inevitable due to the depletion of fossil fuel resources. Biodiesel is an attractive alternative fuel derived from natural oils and can be used directly in diesel engines with no major change. However, various biodiesels may exhibit different performance behaviors and emission characteristics, with some performing worse than diesel fuel. The present research work investigates the performance, combustion, and emission behavior of rubber seed biodiesel (RSB)/diesel blends (B20, B30, B40) and B20 + ethyl hexyl nitrate (EHN) at four injection timings (19°, 21°, 25°, and 27° before top dead center [BTDC]) in a single‐cylinder DI diesel engine. Combustion of biodiesel/diesel blends generally resulted in worse performance, except smoke emission. The addition of EHN reduced hydrocarbon (HC) emissions but negatively impacted brake‐specific fuel consumption (BSFC) and brake thermal efficiency (BTE). However, advanced injection timing not only restored the combustion parameters to the B20 level but also brought them closer to those of the diesel engine. Advancing the injection timing to 27° BTDC improved BSFC and BTE by 3% and 4% compared to B20, respectively. Additionally, the HC emission decreased strongly by 80% and 73%, and smoke emission decreased by 15% and 16%, respectively, compared to B20 and diesel fuel values. A slight improvement in NOx emissions (by 2%) was also observed compared to B20. An increase in cylinder pressure from 66.2 to 67.4 bar was observed with advanced injection timing, contributing to improved engine performance. Analysing of combustion characteristics showed that RSB/diesel blends, when doped with EHN, offer better performance at advanced injection timings making them a suitable alternative fuel to replace diesel fuel usage in developing countries like India.

Photovoltaic (PV) panels play a pivotal role in advancing renewable energy adoption by offering a clean and sustainable alternative to fossil fuels. However, elevated operating temperatures diminish PV cell performance, reducing energy output and accelerating material wear. This research evaluates the cooling efficiency of a PV panel equipped with a three‐dimensional oscillating heat pipe (3D‐OHP) integrated with hybrid nanofluids consisting of graphene oxide–copper oxide (GO–CuO), carbon nanotube–CuO (CNT–CuO), and multiwalled CNT–CuO (MWCNT–CuO). The OHP is charged with two concentrations of each nanofluid, specifically 0.1 and 0.2 g/L, to evaluate their impact on the thermal management of the PV panel. The study involved experimental tests using two PV panels: one equipped with a 3D‐OHP as the cooled panel and the other as a reference panel under identical environmental conditions. Hybrid nanofluids were prepared by dispersing nanoparticles in a base fluid, and their thermal properties were characterized prior to use. Energy and exergy analyses quantify the enhancements in thermal efficiency and the reduction in entropy generation. Experimental results reveal that CNT–CuO with a concentration of 0.2 g/L remarkably improves the electrical power output by 12.07%, outperforming other studied systems with the maximum exergy efficiency of 31.2%. The findings also highlight notable gains in first‐law efficiency. Furthermore, the levelized cost of energy (LCOE) and levelized cost of storage (LCOS) are analyzed, demonstrating the economic feasibility of hybrid nanofluid‐based cooling for PV systems.

The aggregate demand flexibility of a set of residential thermostatically controlled loads (TCLs) can be represented by a virtual battery (VB) in order to manage their participation in the electricity markets. For this purpose, it is necessary to know in advance and with a high level of reliability the maximum power that can be supplied by the aggregation of TCLs. A probability function of the power that can be supplied by a VB is introduced in this paper. This probability function is used to predict the demand flexibility using a rigorous experimental probabilistic method based on a combination of Monte Carlo simulation and extremum search by bisection (MC&ESB) algorithm. As a result, the maximum flexibility power that a VB can provide is obtained. MC&ESB performs the demand flexibility prediction with a given confidence level and taking into account TCLs and users’ real‐time constraints, which is a novel contribution. The performance and validity of the proposed method are demonstrated and discussed in three different case studies where a VB bids its aggregate power in the Spanish electricity balancing markets (SEBMs).

This study evaluates alternatives using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) methodologies across five scenarios (SC1 to SC5), aiming to compare the effectiveness of both approaches in integrating environmental and technical criteria. The results indicate that, in SC1, AHP assigns weights of 14.35% to A1 and 16.22% to A2, while FAHP demonstrates greater dispersion, highlighting A6 with 35.22%. In SC2, AHP prioritizes A1 with 14.16%, whereas FAHP increases the weight of the environmental criterion to 21.18%. In SC3, A1 remains the preferred option in both methodologies, with close weights of 34.00% for AHP and 32.98% for FAHP. In SC4, both methods show similar trends, with A1 standing out at 11.12% and A4 at 34.87%. Finally, in SC5, AHP allocates 8.52% to A1, while FAHP evaluates it at 10.73%. The findings suggest that FAHP allows greater sensitivity to variations in sub‐criteria, enabling a more precise evaluation aligned with sustainability objectives. The significance of environmental and social criteria across the scenarios underscores the necessity of incorporating more sustainable approaches into decision‐making processes. It is concluded that, while AHP delivers consistent results, FAHP may be better suited for contexts characterized by complexity and uncertainty. Furthermore, sensitivity analysis is recommended to examine how variations in criterion weights impact final decisions.