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The goal of this study is to develop a clean and efficient thermal unit for a generator set (gen-set) rated at 80 kW, exploring the potential of Dual-Fuel (DF) combustion (Natural Gas-Diesel) on high-speed Compression Ignition (CI) engines. Typically, the most comparable commercial gen-sets are made up of Heavy-Duty (HD) Diesel engines, whose cost and complexity will probably increase to meet more stringent emissions standards. The conversion of a light-duty Diesel engine may permit to match the high efficiency of Diesels with the low emissions of DF combustion at an affordable cost. Moreover, the new thermal unit would be more compact and lighter. Running on Natural Gas (NG) is less expensive than using Diesel fuel, and it offers more opportunities to reduce the environmental impact (e.g., NG can be easily obtained from biomass, in the same site where the gen-set is installed). Last but not the least, in case of interruption of NG supply, the system can be easily switched to conventional Diesel operation, offering a higher fuel flexibility. Despite the large number of scientific publications concerning DF engines, very few of them consider high-speed units equipped with modern Common Rail injection systems. Even more limited are the investigations on the combustion process at medium-high loads (BMEP > 10 bar), carried out by measuring in-cylinder pressure and optimizing all the fundamental control parameters (injection strategy for both Diesel fuel and NG, boost pressure, EGR rates, etc.). It should be observed that the use of state-of-the-art injection systems and the accurate calibration of their parameters at each operating condition is the only way to maximize the benefits of NG in terms of reduction of soot emissions while addressing the well-known issues related to the increase of some pollutants (HC, CO, and NOₓ). This study reviews the results of a theoretical and experimental activity carried out on a four-cylinder, Common Rail, 2.8-liter turbocharged Diesel engine. A gas injection system is installed upstream of the intake plenum, and an open Electronic Control Unit (ECU) is used to calibrate all the most important engine parameters. Thanks to the deep insight into the combustion process provided by in-cylinder pressure analysis and measurement of pollutant emissions, the study presents some general guidelines for setting the control strategy in this type of DF engine. Considering the operating condition at maximum power (BMEP = 12 bar, 3000 rpm, brake power = 83 kW), the following advantages are observed with comparison to the standard Diesel engine: soot is more than halved, NOₓ emissions are reduced by 32% and CO₂ by 31%, and Brake Thermal Efficiency (BTE) increases from 35.8% to 39%. The only drawback is the increase of one order of magnitude of both CO and HC, requiring a specific oxidation catalyst. Another outcome of the study is the limitation on the use of DF NG-Diesel combustion at low loads: the experimental activity demonstrates that it is very difficult to achieve complete combustion of an ultra-lean air-NG premixed charge so that BTE tends to drop. At these conditions, it appears to be more convenient to switch back to standard Diesel operations.

This paper proposes an energy management strategy (EMS) for a hybrid stand-alone plant destined to supply controllable loads. The plant is composed of photovoltaic panels (PV), a wind turbine, a diesel generator, and a battery bank. The set of the power sources supplies controllable electrical loads. The proposed EMS aims to ensure the power supply of the loads by providing the required electrical power. Moreover, the EMS ensures the maximum use of the power generated by the renewable sources and therefore minimizes the use of the genset, and it ensures that the batteries bank operates into the prefixed values of state of charge to ensure their safe operation. The EMS provides the switching control of the switches that link the plant components and decides on the loads’ operation. The simulation of the system using measured climatic data of Mostoles (Madrid, Spain) shows that the proposed EMS fulfills the designed objectives.

The paper discusses the problem of cooperation between multiple model predictive control (MPC) systems. This approach aims at improving the control quality in electrical energy generation and forms the next step in a series of publications by the authors focusing on the optimization and control of electric power systems. Cooperation and cooperative object concepts in relation to a multi MPC system are defined and a cooperative control solution for a nuclear power plant’s turbine generator set is proposed. The aim of enabling information exchange between the controllers is to improve the performance of power generation. Presented and discussed simulation tests include various variants of information exchange between the turbine and synchronous generator MPC controllers of the nuclear power plant.

The development of AC distribution systems provides for the seamless integration of low-voltage microgrids with distributed energy resources (DERs). This poses new challenges for the control of normal, emergency, and post-emergency states of microgrids, calling for the creation and development of information and communications technology infrastructure. Power converters/inverters that are used to integrate renewable DERs lack inertia. Along with them, fossil fuel-fired generation units are also being integrated into microgrids. These include gas generator sets, diesel generator sets, and microturbines, having small (up to 1–2 s) values of mechanical inertia constants—Tj. This leads to an increase in the rate of transients by a factor of 5–10. Under these conditions, the technical requirements for the speed of automatic power flow control systems, as well as the methods they rely on, have to be reconsidered. Microgrids include DC microgrids, AC microgrids, and hybrid (AC-DC) microgrids. In the case of hybrid microgrids, DERs are connected to the DC grid and are integrated into the AC grid through a common inverter. The complexity of the task of microgrid control is due to the need to choose properly the type and extent of control actions so as to prevent the emergence and development of accidents. The employed control methods must ensure the reliable power supply to consumers and the quality of power in microgrids, as well as the reliable operation of the external distribution systems into which they are integrated. The article gives an overview of control methods for low-voltage AC and AC-DC microgrids, which allow one to tackle effectively solve the tasks.

Inrecent electrical power networks a number of failures due to overloading of the transmission lines, stability problems, mismatch in supply and demand, narrow scope for expanding the transmission network and other issues like global warming, environmental conditions, etc. have been noticed. In this paper, a thyristor-controlled series compensator (TCSC) is placed at the optimum position by using two indices for enhancing the power flows as well as the voltage security and power quality of the integrated system. A fusedseverity index is proposed for the optimal positionalong with a grey wolf algorithm-based optimal tuning of the TCSC for reduction of real power losses, fuel cost with valve-point effect, carbon emissions, and voltage deviation in a modern electrical network. The voltage stability index to evaluate the power flow of the line and a novel line stability indexto assessthe line capacityare used. The TCSC is placed at the highest value of the fusedseverity index. In addition, an intermittent severity index (IMSI) is used to find the most severely affected line and is used for relocating the TCSC to a better location under different contingencies.Lognormal and Weibull probability density functions (PDFs)are utilized forassessing the output ofphotovoltaic (PV) and wind power. The proposed methodhas been implemented on the IEEE 57 bus system to validate the methodology, and the results of the integrated system with and without TCSC are comparedunder normal and contingency conditions.

In this study, a new meta-heuristic optimization method inspired by the behavioral choices of animals and hunger-driven activities, called hunger games search (HGS), is suggested to solve and formulate the single- and multi-objective optimal power flow problem in power systems. The main aim of this study is to optimize the objective functions, which are total fuel cost of generator, active power losses in transmission lines, total emission issued by fossil-fueled thermal units, voltage deviation at PQ bus, and voltage stability index. The proposed HGS approach is optimal and easy, avoids stagnation in local optima, and can solve multi-constrained objectives. Various single-and multi-objective (conflicting) functions were proposed simultaneously to solve OPF problems. The proposed algorithm (HGS) was developed to solve the multi-objective function, called the multi-objective hunger game search (MOHGS), by incorporating the proposed optimization (HGS) with Pareto optimization. The fuzzy membership theory is the function responsible to extract the best compromise solution from non-dominated solutions. The crowding distance is the strategies carried out to determine and ordering the Pareto non-dominated set. Two standard tests (IEEE 30 bus and IEEE 57 bus systems) are the power systems that were applied to investigate the performance of the proposed approaches (HGS and MOHGS) for solving single and multiple objective functions with 25 studied cases using MATLAB software. The numerical results obtained by the proposed approaches (HGS and MOHGS) were compared to other optimization algorithms in the literature. The numerical results confirmed the efficiency and superiority of the proposed approaches by achieving an optimal solution and giving the faster convergence characteristics in single objective functions and extracting the best compromise solution and well-distributed Pareto front solutions in multi-objective functions.

Bolt loosening is a common mechanical failure in large-scale generator sets, especially the top cover bolts, which can lead to unstable operation, affecting the safety and efficiency of the unit. Real-time monitoring of bolt pre-tightening force and the early diagnosis of loosening are crucial for ensuring the long-term safe operation of the generator set. This study proposes a novel method for diagnosing faults in large-scale hydraulic units, with a particular focus on thread loosening failures. The proposed approach utilizes stress feature analysis and intelligent algorithms to enhance the diagnostic process. The study involves a detailed analysis of the stress transfer mechanism during bolt loosening, aiming to elucidate the relationship between changes in pre-tightening force and stress distribution in the surrounding bolts. A combined approach of monitoring the pre-tightening force and signal analysis is employed to facilitate the real-time tracking of dynamic changes. Experimental results show that loosening a single bolt causes stress distribution changes in the entire bolt group, particularly in the adjacent bolts. The study also introduces a diagnosis method combining pre-tightening force changes and VMD (Variational Mode Decomposition), which proves to be highly accurate in locating loosened bolts. Engineering applications validate that VMD analysis combined with the Spearman method effectively identifies changes in stress and has high accuracy and potential for diagnosing bolt loosening failures, providing valuable guidance for generator set maintenance.

To address the issues of reduced operational efficiency, shortened equipment lifespan, and significant safety hazards caused by bearing wear and blade cavitation in hydroelectric turbine generators due to prolonged high-load operation, this paper proposes a ResNet + self-attention-based acoustic fingerprint fault diagnosis algorithm for hydroelectric turbine generators. First, to address the issue of severe noise interference in acoustic signature signals, the ensemble empirical mode decomposition (EEMD) is employed to decompose the original signal into multiple intrinsic mode function (IMF) components. By calculating the correlation coefficients between each IMF component and the original signal, effective components are selected while noise components are removed to enhance the signal-to-noise ratio; Second, a fault identification network based on ResNet + self-attention fusion is constructed. The residual structure of ResNet is used to extract features from the acoustic signature signal, while the self-attention mechanism is introduced to focus the model on fault-sensitive regions, thereby enhancing feature representation capabilities. Finally, to address the challenge of model hyperparameter optimization, a Bayesian optimization algorithm is employed to accelerate model convergence and improve diagnostic performance. Experiments were conducted in the real working environment of a pumped-storage power station in Zhejiang Province, China. The results show that the algorithm significantly outperforms traditional methods in both single-fault and mixed-fault identification, achieving a fault identification accuracy rate of 99.4% on the test set. It maintains high accuracy even in real-world scenarios with superimposed noise and environmental sounds, fully validating its generalization capability and interference resistance, and providing effective technical support for the intelligent maintenance of hydroelectric generator units.

Power conversion interface for small-capacity wind power generation system based on permanent-magnet synchronous generator (PMSG) is proposed in this paper. The proposed power conversion interface will convert the wind power generated from the three-phase PMSG to a high quality power to inject into the single-phase utility. This power conversion interface comprises of a power converter and a zero-sequence transformer set. The power converter is controlled to generate a set of positive-sequence currents to absorb a real power from the three-phase PMSG and a set of zero-sequence currents to pass through the zero-sequence transformer set to the single-phase utility. A feed-forward control is used to control the proposed power conversion interface so as to simplify the control circuit. A simplified maximum power point tracking method is also proposed and incorporated in the control circuit of the power conversion interface to extract the maximum power of the PMSG-based wind power generation system. Hence, the proposed power conversion interface has the advantages of simplifying both the power circuit and the control circuit. A prototype is developed to demonstrate the performance of the proposed power conversion interface. The experimental results show that the proposed power conversion interface can achieve the expected performance.

This paper discusses the issue of optimizing the communication between the components of a cooperating control system formed by a pair of MPC controllers of a nuclear power plant turbine set using online recursive least squares identification. It is proposed to use event-triggered communication, i.e., sending information only at selected time instants, as opposed to the standard approach where communication is triggered by time (time-triggered approach). The aim of this paper is to propose a change in the method of information exchange in the case of asynchronous communication between control system components and to prove its suitability for the selected application. Resignation from continuous communication in favor of sending information only at selected moments allows the load on the communication network to be reduced by approximately 90% while maintaining the quality of control.