Editorial: Special Issue “Thermo-Mechanical and Electrical Measurements for Energy Systems: 1st Edition”

Over the last decade, rapid technological advances have accelerated the development and application of various measurement solutions in energy systems. Koltunowicz et al. [1] investigated the mechanical, thermal, and electrical properties of insulating bio-oils for power applications, identifying key parameters for improved moisture assessment. For integrated energy systems, Ma et al. [2] suggested a multi-time-scale frequency control method that improves dynamic response and coordination under stochastic conditions. Finally, Pălăcean et al. [3] introduced a smart IoT-based power meter suitable for both industrial and domestic contexts, capable of capturing standard and power quality metrics with high accuracy.

The ongoing transformation of global energy systems—driven by the growing integration of renewable sources, digitalization, and the pursuit of efficiency and resilience—demands continuous innovation in how energy flows are monitored, measured, and controlled. For example, Apa et al. [4] developed an automated system to simulate real battery cycles in domestic renewable contexts, both for short applications and over 24-h domestic grid scenarios [5]. Negri et al. [6] provided a metrological evaluation of AI-based state-of-charge estimations, and Zulkifli et al. [7] analyzed a solar-powered aquaponics irrigation system designed for home gardening. Hilger et al. [8] contributed a standardized approach to short-term energy measurements in SMEs using mobile technologies. To enhance system efficiency, D’Alvia et al. [9] proposed a scaled test bench for measuring hybrid powertrain performance, and Cava et al. [10] designed a testing platform for hydrogen-powered hybrid trains. In terms of resilience, Baghbanzadeh et al. [11] investigated distributed generation strategies for multi-microgrid networks. Additionally, Wei et al. [12] proposed an intelligent control method to optimize solar energy performance under shading, and Chahtou et al. [13] examined the selection of efficient PV modules based on degradation and failures in various Algerian cities. For design optimization, Yin et al. [14] introduced a grid energy storage planning method based on an optimized butterfly algorithm, while Araghian et al. [15] addressed performance under variable conditions and user comfort. Finally, Zhang et al. [16], Morozova et al. [17], and Narayanan et al. [18] explored integrated and distributed energy systems in residential, industrial, and predictive control applications, respectively, highlighting the vital role of combined thermal-electrical measurements in diagnostics and system operation.

The application of AI and machine learning algorithms in signal processing and control theory has significantly enhanced the management of modern energy systems. For instance, Senyuk et al. [19] developed a deep learning-based emergency control methodology using synchronized measurements for power systems, while Al-Falahi et al. [20] proposed a hybrid optimization algorithm tailored to hybrid energy systems on electric ferries. Ibrahim et al. [21] provided a broad overview of machine learning applications in smart electric power systems, highlighting emerging perspectives and trends. Complementing these control strategies, modern metrology has advanced through integrated AI models: Pratticò et al. [22] combined LSTM and U-Net models to monitor electrical absorption via sensor networks; Shabangu et al. [23] modeled bioenergy production in microbial fuel cells using MATLAB/Simulink; and Kfouri and Margossian [24] introduced a resilient deep learning approach for state estimation in distributed grids. These computational advancements support the accurate interpretation of thermo-mechanical quantities, as demonstrated by Narayanan [25], who evaluated a predictive controller for integrated thermal–electrical systems, and Verra et al. [26], who analyzed the impact of improved measurements on smart thermal energy systems. Similarly, Orosz et al. [27] validated an integrated model of electrical and thermal energy demand for rural health centers. Finally, non-electrical parameters also play a vital role. Pattanaik et al. [28] critically reviewed the deployment of phasor measurement units (PMUs) and their implications in smart grids, while Smolkin et al. [29] examined the thermal and economic performance of combined heat and power (CHP) plants.

This Special Issue, “Thermo-Mechanical and Electrical Measurements for Energy Systems: 1st Edition,” presents thirteen original contributions that highlight the interdisciplinary and application-oriented nature of current research in this field. The papers span a variety of hot topics, including smart grid instrumentation, addressed by Ezhilarasi et al. [30], as well as advanced smart prepaid metering systems and a comprehensive review of smart meter technologies by Beno et al. [31], and reactive power analysis explored by Parada [32]. Furthermore, several works focus on novel control and optimization algorithms in both electrical and thermal domains: Chakravarthi et al. [33] propose an IoT-based energy meter to enhance grid intelligence, Sarıışık and Öğütlü [34] assess machine learning performance for hybrid power forecasting, and Akinte and Plangklang [35] develop an autonomous energy controller supported by econometric modeling for reserve generation networks.

Six contributions focus on advanced measurement techniques in electrical systems. Hawron et al. [36] explore the evaluation of transient active power within two-terminal networks, a phenomenon that occurs when current and voltage signals are quasi-sinusoidal. First, a set of simulations is performed for various types of transient states. Then, a comparison is drawn between the idealized case and measurement data obtained from a laboratory-based voltage sag model, demonstrating the influence of disturbance power on the total power within a real power system. Jaraczewski et al. [37] propose a novel algorithm for steady-state analysis of electromagnetic field-circuit models with nonlinear conductive materials. By representing solutions using Fourier series and applying discrete differential operators, the method converts PDEs and ODEs into algebraic equations. Validation on a 2D solenoid model demonstrates its accuracy and effectiveness. Sołjan and colleagues [38,39,40] extend Budeanu and CPC power theories to analyze and compensate reactive and distortion currents in both three-phase and single-phase systems, proposing passive compensation methods and exploring their impacts on reactive power and distortion components. In Ref. [38], they expose and extend Budeanu’s power theory to three-phase four-wire systems with symmetrical non-sinusoidal voltages, integrating concepts from the CPC theory. A comprehensive mathematical model is developed, deriving equations for reactive and complementary reactive currents and analyzing their independence. In Ref. [39], building on previous results, they propose a framework to analyze and compensate for distortion and reactive currents. Five passive compensation methods are reported, including strategies that also address unbalanced currents. In Ref. [40], they explore passive reactive power compensation in single-phase systems using capacitors, chokes, or LC structures, aiming to minimize reactive current. The extended Budeanu theory enables parameter selection and analysis of distortion power increases based on compensatory structure. Kurkowski et al. [41] analyze the hidden operating costs in road lighting, particularly those linked to capacitive reactive energy caused by modern LED luminaires that lack built-in compensation. It highlights how compensation systems, while reducing reactive power, introduce additional active power losses. Measurement results and a case study audit illustrate these effects, guiding modernization decisions.

In the area of grid-integrated systems, Piesciorovsky et al. [42] compare the performance of an advanced medium-voltage sensor with traditional PTs and CTs using a testbed at Oak Ridge National Laboratory. The sensor uses a capacitive divider and Rogowski coil to monitor voltage and current, respectively. Its accuracy is evaluated under various grid conditions, analyzing signal behavior, harmonics, and distortion. Kuboń et al. [43] analyze how agricultural biogas plants affect power quality, particularly voltage distortion at the grid connection point. Although theory suggests that biogas generation should reduce voltage distortion, field tests at three plants showed this effect only when the biogas plant was connected near a substation. However, increased generation consistently reduced current distortion, indicating optimal performance near rated power.

On the thermal and mechanical sides, Paranchuk et al. [44] present a fuzzy control model for regulating arc length in high-power arc steelmaking furnaces. Using a three-component Sugeno-based fuzzy inference system, the model improves control dynamics and energy efficiency. Simulations show it outperforms traditional differential models by reducing electrical losses and increasing average arc power. Lech et al. [45] propose a methodology for testing low-current vacuum arcs using an innovative vacuum discharge chamber with an internal contact system. Experiments conducted at 230 V and 5 A under atmospheric and high-vacuum pressures demonstrated that vacuum insulation significantly reduces arc energy and burning time. These findings support the design of vacuum switchgear to minimize arc-related damage in contact gaps. Mazgaj et al. [46] focus on a method to calculate flux density changes in transformer steel sheets for any magnetization direction, accounting for anisotropy in core corners and joints. It uses limiting hysteresis loops for rolling and transverse directions to model magnetic behavior. Calculated results closely match measured hysteresis loops across various directions.

From a control and optimization perspective, Ullah et al. [47] due to unbalanced loads in three-phase four-wire systems cause unequal currents and heavy neutral wire currents, leading to losses and constraints, proposes a practical load balancing method using a moth–flame optimization (MFO) algorithm to compensate neutral wire current by reconfiguring single-phase loads. The proposed approach effectively reduces the neutral current to near zero, thereby improving system performance. They also proposed a real case application at the ET Peshawar Bannu Campus. Pazderski et al. [48] investigate an LQR-based control approach for nonlinear systems using smooth state and input transformations, applied to stabilizing a Pendubot. It demonstrates that equivalent dynamics can be achieved near the set point across different state-space representations. Simulation and experimental results validate the suboptimal controllers, providing practical methods for stabilizing robotic systems.

These papers demonstrate the centrality of measurement and data-driven approaches in advancing sustainable [49,50,51] and intelligent energy systems [52,53].

We thank all authors for their valuable contributions and the reviewers for their insightful feedback. We hope that this Special Issue will inspire further research at the intersection of electrical, thermal, and mechanical domains and support the development of more efficient and resilient energy infrastructures.