Modern Power Research Articles

Аdaptive Сontrol algorithm Based on a Virtual Synchronous Generator. Part II

An increase in the penetration level of power plants based on renewable energy sources using power converters (PCs) has a direct impact on the dynamic characteristics of modern electric power system (EPS) and, as a consequence, the nature of the transient processes. One of the main problems in such EPS is a significant change in the magnitude of the total inertia of the system over time, which leads to an increase in the rate of change of frequency and the magnitude of its maximum deviation under various disturbances. A promising direction for solving this problem is the synthesis of new structures of PC control systems based on a virtual synchronous generator (VSG) with adaptively changing parameters. The results of the research in this area are presented in the paper, which consists of two parts. In the first part of the paper, the dependence of the adaptive algorithms efficiency for controlling the parameters of the VSG on the structure used is substantiated. A comparative analysis of the developed modified VSG structure with traditional algorithms is performed and its fundamental advantages are proved. The second part of the article presents an analysis of the influence of the modified structure parameters of the VSG on the dynamic response using time domain transient analysis. Based on the results obtained, adaptive algorithms for independent control of virtual inertia and parameters of the VSG damper winding have been developed. The performed mathematical modeling confirmed the reliable and efficient operation of the developed adaptive control algorithms and the modified structure of the VSG as a whole. From the theoretical and experimental results obtained in the paper, it follows that there is a need for simultaneous development and improvement of adaptive control algorithms and the VSG structures used for this.

An ML-Based Solution in the Transformation towards a Sustainable Smart City

The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors’ own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities.

USE OF IIoT TECHNOLOGIES IN ENGINE MONITORING AND CONTROL SYSTEMS

Determining the operating parameters of engine systems is very important to ensure its long-term reliability. Modern power plants use a large number of modern electronic components (sensors, controllers, etc.). All systems generate a huge volume of data that must be tracked, processed and analyzed, which in turn creates calls to gateways and servers. However, the current state of development of microcontrollers and their power allow the use of fog computing, which can lead to more efficient use of both the power plant and data transmission systems. Also, the use of modern microcontrollers and fog computing allows modernizing old power plants with minimal effort and creating new information nodes for monitoring the state of nodes and engine systems and easily combining them with systems already existing on the engine. The selection of a microcontroller and justification of its type for the modernization of the fuel system of a diesel engine are considered. Rapid processes occur in the high-pressure fuel system of diesel engines, which can be determined by changes in fuel pressure. For detailed measurement of processes in the fuel system, measurements should be made with a frequency of at least 24 kHz, i.e.: if the crankshaft rotation frequency is 4000 min-1, then 24,000 measurements should be made every second (when registering data after 1 degree of crankshaft rotation). Another indicator that imposes its requirements is the bit rate of the analog-to-digital converter (ADC), which depends on the range of recorded values. For example, an ADC with a resolution of 8 bits is capable of outputting 256 discrete values (0…255). That is, the higher the bit rate, the more sensitive the converter is to signal changes, and 8 bits may not be enough. Thus, even a small number of sensors can generate a huge amount of data. Transmitting large amounts of data is irrational and can lead to data loss and "clogging" of transmission channels, which challenges the computational performance of the microprocessor.

Binary blends of poly(lactic acid) and poly(methyl methacrylate) for high energy density and charge/discharge efficiency capacitors

Polymer dielectrics are widely used in modern power electronics due to their high flexibility and high breakdown strength. However, the limited energy density of current polymer dielectrics limits their wider applications, and there is an urgent need to develop novel polymer dielectric materials. Poly(lactic acid) (PLA) is favored for biological applications due to its biocompatibility and biodegradability. In general, PLA has three optical isomers, namely poly(L-lactide) (PLLA), poly(D-lactide) (PDLA), and poly(DL-lactide) (PDLLA), but the investigation of their dielectric properties remains limited. In this study, a significant increase in energy storage density and charge/discharge efficiency in poly(methyl methacrylate) (PMMA) was achieved by incorporating isomers of PLA into PMMA. Experimental results indicate that the introduction of PLA creates a phase-separated structure within PMMA, and in particular, the introduction of the crystalline region significantly improved the breakdown strength (Eb). Finally, PLLA/PMMA 50/50 and PDLA/PMMA 50/50 exhibit the discharged energy densities of 8.55 J cm−3 and 8.18 J cm−3, respectively, with charge/discharge efficiencies of 89.6% and 90.9%. This work enables the achievement of all-organic dielectrics with high energy storage density and high efficiency through the construction of phase-separated structures and demonstrates the great potential of biodegradable polymers in electronic devices.

Power system event detection using Teager Kaiser Energy Operator and autoencoder based algorithm

As the global demand for electricity increases, modern power systems are operating closer to their stability limits. This increases power system events that vary in intensity. It is essential to detect these power system events as early as possible and to take the necessary control actions to prevent escalation. This paper proposes a power system event detection method based on the Teager Kaiser Energy Operator (TKEO) and Autoencoder. In the proposed method, the rules-based algorithm performs primary detection, and the detected events are confirmed using the TKEO and autoencoder-based algorithms. The effectiveness of the proposed method was tested using test signals, simulated power system signals, and real-time power system data obtained from an actual power system. The results reveal the robustness and accuracy of the proposed method in detecting single and multiple power system events in the PMU data.

Performance analysis of a new single-phase transformerless PV inverter structure based on a buck-boost converter

A single-phase inverter based on a buck-boost converter is increasingly used in modern power electronics, particularly in solar photovoltaic systems. Unlike traditional inverters that utilize transformers to electrically isolate the solar system from the power grid, this transformerless design enhances efficiency, reduces weight, and lowers costs. However, the absence of a transformer introduces challenges related to Electromagnetic Compatibility (EMC) and the potential for increased leakage currents, which must be carefully managed to ensure system safety and performance. In this paper, the performances of a new configuration of a single-phase transformerless PV inverter based on a dc-dc buck-boost converter is proposed and analyzed. Since it is the only switch (mosfet) in the DC-DC converter that operates at high frequency, and it is controlled by pulse width modulation (PWM) to adjust the output voltage. This allows for flexible voltage conversion, enabling the inverter to either step up (boost) or step down (buck) the input voltage as needed. Therefore, switching losses can be significantly reduced to improve efficiency and reduce electromagnetic interference of the single-phase inverter structure. In order to demonstrate the EMC performances and evaluate the leakage current of the proposed structure, an EMC model is developed under the Simscape-MATLAB/Simulink environment, this model offers an EMC analysis including the prediction of leakage current over a frequency range from 100 Hz to 2 MHz. The results clearly showed that the level of the Total harmonic distortion (THD), as well as the levels of leakage current, are below the requested EMC standards.

VOLTAGE FLICKER MITIGATION USING GTOBASED STATCOM TO IMPROVE POWER QUALITY OF A MULTI MACHINE SYSTEM

Transmission networks of modern power systems are becoming increasingly stressed because of growing demand and restrictions on building new lines. One of the consequences of such a stressed system is the threat of losing stability following a disturbance. Flexible ac transmission system (FACTS) devices are found to be very effective in a transmission network for better utilization of its existing facilities without sacrificing the desired stability margin. FACTS such as Static Synchronous Compensator (STATCOM), employ the latest technology of power electronic switching devices in electric power transmission systems to control voltage and power flow. The STATCOM adjusts voltage at its terminal by managing the amount of reactive power injected into or absorbed from the power supply. When the system voltage is low, STATCOM creates reactive power; when the system voltage is high, STATCOM absorbs reactive power. In this paper STATCOM controllers are designed for improving transient stability of multi machine systems. Proposed controllers are implemented under MATLAB/SIMULNK environment. Results of PI based controllers installed with multi machine system is found to be better on comparison with conventional system.

An Enhanced Continuation Power Flow Method Using Hybrid Parameterization

The rapid integration of renewable energy sources and the increasing complexity of modern power systems urge the development of advanced methods for ensuring power system stability. This paper presents a novel continuation power flow (CPF) method that combines two well-known parameterization techniques: natural parameterization and arc-length parameterization. The proposed hybrid approach significantly improves computational efficiency, reducing processing time by 32.76% compared to conventional methods while maintaining high accuracy. The method enables faster and more reliable stability assessments by efficiently managing the complexities and uncertainties, particularly in grids with high penetration of renewable energy.

Which coal-fired power units in China should be prioritized for decommissioning?

China's total installed coal power capacity is larger than that of all other countries combined. However, for China to attain its objectives of carbon peak in 2030 and carbon neutrality in 2060, a considerable number of these units will have to be phased out. Therefore, it is crucial to identify which coal-fired power units should be prioritized for decommissioning. This study constructs a stacking evaluation model comprising multiple sub-models, based on the power resource endowment, power generation efficiency, and social responsibility. This model is employed to assess each existing coal-fired power unit in China, with the objective of determining which units should be prioritized for retirement. Additionally, the impact on power supply and heating systems in various Chinese provinces is assessed based on these decommissioning scenarios. The results show that: ① Compared to a single model, the stacking evaluation model developed in this study exhibits higher accuracy and robustness. ② China should prioritize the phasing out of small, inefficient coal-fired power units that do not supply heat and are located in regions with a higher proportion of thermal power generation. Approximately 23.1 % (679 units) of coal-fired power units will need to be retired by 2030. ③ The retirement of outdated coal-fired power units will lead to a more efficient, modern, and healthier Chinese power system. ④ The closure of 679 outdated coal-fired units will not result in a shortage of electricity but will substantially affect urban heating. Northern provinces reliant on district heating anticipate a loss of 516.42 million GJ/year in heating capacity.

Preface

Welcome to the Proceedings of the 2024 3rd International Conference on the Energy Internet and Energy Interactive Technology (EIEIT 2024) held via virtual form in Xi’an, China during May 10th to 12th, 2024! EIEIT 2024 builds upon the success of its two previous editions, underscoring the growing importance of energy internet as a cornerstone technology supporting modern power systems. However, despite its promising potential, the development of energy internet in China remains in its nascent stages, necessitating a concerted effort from academia, industry, and government to propel its progress. The conference was organized to provide a comprehensive forum where participants could present their latest research findings, discuss challenges, and explore opportunities for collaboration. The conference attracted about 80 scholars and researchers from all over the world, covering representatives from academia, industry, research institutes, and government organizations. They have a wide range of professional backgrounds related to energy and power engineering. Such a composition of participants not only facilitated interdisciplinary exchanges and cooperation, but also injected a wealth of academic ideas and perspectives into the conference. The program of EIEIT 2024 is rich and diverse, including keynote speeches, oral presentations, poster presentations, academic discussion, etc. The keynote speeches were delivered by internationally renowned scholars including Professor Sanjeevikumar Padmanaban (University of South-Eastern Norway, Norway) and Associate Professor Cristian Paul Chioncel (Universitatea Babeș-Bolyai, Romania) who have in-depth discussions on the cutting-edge dynamics and future development of energy internet and energy interactive technology; the oral presentations provided an extra platform for researchers to showcase their latest research results; and the poster presentations further promoted academic exchanges and collisions of ideas. These conference agendas were designed to promote international academic exchanges and cooperation, and to push forward the researches and applications in the field of energy internet and energy interactive technology. List of Committee Member is available in this Pdf.

Optimal utilization of frequency ancillary services in modern power systems

The widespread global adoption of wind energy sources has established a significant presence in the existing power grid. However, the massive integration of intermittent wind energy poses forecasting errors, prompting the need for supplementary reserves from conventional energy sources with increased operational expenses and carbon emissions. Hence, to facilitate the seamless operation of large-scale wind-integrated power grids, it is imperative to harness the potential of renewable energy sources and leverage flexible loads to deliver power-balancing services. In this research, dynamic real-time power dispatch strategies have been developed for the Automatic Generation Control (AGC) system to integrate the reserve capacities of conventional generation units and wind power plants and utilize the demand response capabilities of flexible loads for power balancing services. A comprehensive power system grid model was developed in DigSilent PowerFactory software, consisting of coal-based energy systems, wind energy systems, gas turbines, and cold storage units as flexible loads. The study is divided into different case studies to assess the impact of each scenario on system operation in mitigating the forecasting errors of wind power plants. Further, a comparative analysis was performed to illustrate the effectiveness of each case study. The analysis showed that Case Study III, where reserves are provided by coal energy systems and cold storage units, yielded the highest reduction in Positive Regulation (PR) and Negative Regulation (NR) errors, at 89.0% and 94.15%, respectively. Conversely, Case Study IV demonstrated the least reduction in errors, with 67.82% in PR and 78.41% in NR. However, it indicates that reserves can be supplied from wind energy systems and flexible loads without the support of conventional power plants.

Bidirectional long‐short‐term memory‐based fractional power system stabilizer: Design, simulation, and real‐time validation

AbstractPower oscillations in modern power grids are inherent phenomena that may threaten system reliability. Therefore, to ensure acceptable system reliability, effective damping of power oscillations is inevitably required. In this context, this article introduces a novel approach to designing fractional power system stabilizer (FPSS) for effective damping of power oscillations. Bidirectional long‐short‐term memory (Bi‐LSTM) approach is adopted to predict the parameters of FPSS. The conventional phase compensation technique is used to train Bi‐LSTM network. To validate the efficacy of FPSS, different test scenarios of contingent operating conditions are simulated for the system. Comparative analysis is carried out with conventional power system stabilizers (PSSs) and optimization‐based PSS techniques. Additionally, a test scenario is performed against existing deep neural network‐based PSS methods to ascertain the robustness of the proposed PSS. Furthermore, the performance of the proposed Bi‐LSTM‐based FPSS is validated in real‐time simulation using an interfaced OPAL‐RT OP5700 hardware device.

Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses

Accurately predicting electricity load is crucial for maintaining the stability of modern power systems. Most studies focus on deterministic predictions, while uncertainty prediction and demand responses (DR) need further research. This paper proposes Bilinear-DRTFT, a load forecasting model that supports uncertainty and considers DR. To comprehensively evaluate model performance, this study combines five deterministic evaluation metrics and three uncertainty evaluation metrics on the test set. The research findings include:1. Incorporating DR effectively enhances the model's performance in both deterministic and uncertainty prediction; 2. Model performance continually improves with refined DR, achieving better uncertainty reduction when incorporating dynamic DR signals considering critical peak electricity prices, reaching expected performance at 80 % and 90 % confidence levels with narrow PINAW; 3. After adding DR, high-quality feature engineering provides the basis for the simplification of feedforward network; 4. Analyzing monthly uncertainty predictions reveals specific time periods on which DR policies should focus. These conclusions are validated in two experimental regions.

Optimal Solution of the Dynamic Economic Dispatch by Improved Teaching-Learning-Based Artificial Bee Colony Algorithm

Dynamic economic dispatch is one of the most handled problem in modern power system operations. It aims to optimize the output power from thermal generating units over a specified time period to minimize the total fuel cost, while satisfying the several constraints such as generation limits, ramp rate limits, and power balance. In addition to these constraints, the prohibited operating zones and the valve-point loading effect are included the DED problem. In this case, the complexity, nonlinearity, and non-convexity of the DED problem are increases. Therefore, in order to solve the DED problem, a powerful meta-heuristic search (MHS) algorithm are proposed. In this study, an improved teaching-learning-based artificial bee colony (TLABC) algorithm, where the fitness-distance balance based TLABC (FDB-TLABC) and natural-survivor method based TLABC (NSM-TLABC) algorithms were hybridized. To prove the performance of the proposed algorithm, it was applied to solve the DED problem and benchmark problem suites. In the simulation study carried out on benchmark problems, the results of the proposed algorithm and five MHS algorithms were evaluated statistically. According to Friedman test results, the proposed algorithm ranked first with 2.2836 values among them. On the other hand, the proposed algorithm and its rival algorithms were applied to solve the two DED cases. The results of them show that the proposed algorithm achieved superior performance to find the best objective values for both case studies.

A hybrid YDSE - THDCNN approach based multi objective optimization of energy management for renewable energy sources with electric vehicles

Thermal power plants have long been essential to the fossil fuel-based electricity supply chain, according to statistics studies. Nevertheless, due to the enormous initial and ongoing expenses of modern power plants as well as their negative environmental effects. This manuscript proposes a hybrid technique for grid-connected load-following hybrid photovoltaic and wind microgrid with a grid-connected electric vehicle charging system. The proposed hybrid technique is the joint execution of both Young's double-slit experiment optimizer (YDSE) and the Tree Hierarchical Deep Convolutional Neural Network (THDCNN). Hence, it is named as YDSE-THDCNN. The major objective of the proposed technique is to reduce the fuel, operation, and maintenance costs, as well as to reduce the environmental costs and maximize the efficiency of the system. The proposed YDSE technique is utilized to generate the control signal of the inverter and the optimal signal will be predicted by the THDCNN. By then, the MATLAB working platform has adopted the proposed way, and the existing method is used to compute its execution. The proposed method outperforms any existing techniques, including the Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Particle Swarm Optimization (PSO) Algorithm. The existing method shows the cost of 2.6$, 3.8$, 4.6$, and the proposed method shows the cost of 1.3$ which is lower than the other existing method.

Conceptional design for a universal HVDC-HVAC vacuum-interrupter-based circuit breaker

Modern power systems high voltage transmission systems either HVDC or HVAC has mandated the presence of two types of circuit breakers (CB), HVAC-CB and HVDC-CB. That required two different production lines, higher costs and more complicated manufacturing process. A solution is proposed in this paper which is a concept design for a universal HVCB (UHVCB) that is applicable to both HVDC and HVAC system. Such a concept would allow a faster, easier production and more economical unit cost for CBs that would benefit the entire industry from manufacturers to utilities. The design of HVDC-CB was used as the foundation the proposed UHVCB such that L–C branches are used. The UHVCB was tested in both HVAC and HVDC transmission systems. The results showed the reliable performance of UHVCB in both systems. The recorded transient recovery voltage (TRV) was reduced from 750 to 430 kV when UHVCB was used instead of conventional HVCB. The testing included both technical and economic aspects. The performance of the UHVCB was tested by varying the parameters for L–C shunt branches in both systems. That included varying the value of L in range 0.3–1 mH and 10–30 µF for C. The most important conclusion from this paper that a UHVCB that is applicable in HVDC and HVAC systems is achievable and this paper is only an initial step in achieving this goal.

Optimal operation of soft open point devices and distribution network reconfiguration in a harmonically polluted distribution network

Soft open point (SOP) and distribution network reconfiguration (DNR) are crucial for modern power systems as they address significant challenges in managing power losses and maintaining power quality. SOP is a power electronic device used in distribution networks (DNs) instead of tie lines. It injects reactive power into feeders and transfers active power between them, while DNR involves altering the network topology to improve DN index. In this paper, the effect of DNR, optimal operation of SOP, and simultaneous operation of DNR and SOP are thoroughly investigated and compared on IEEE 33 bus harmonic polluted DN via particle swarm optimization (PSO). These comparisons were performed by considering three loss-based objectives including fundamental and harmonic losses and summation of them, along with one voltage-based objective function assessing voltage stability, and four Total Harmonic Distortion (THD)-based objective functions quantifying harmonic pollution. Simulation results not only demonstrate the importance of optimal operation of SOP in improving losses and power quality but also highlight the synergistic effect of simultaneous operation of DNR and SOP in further enhancing these two objectives. Results provide valuable insights for future research and practical applications in operation and optimizing power distribution systems.

Transformer-based deep learning networks for fault detection, classification, and location prediction in transmission lines

ABSTRACT Fault detection, classification, and location prediction are crucial for maintaining the stability and reliability of modern power systems, reducing economic losses, and enhancing system protection sensitivity. This paper presents a novel Hierarchical Deep Learning Approach (HDLA) for accurate and efficient fault diagnosis in transmission lines. HDLA leverages two-stage transformer-based classification and regression models to perform Fault Detection (FD), Fault Type Classification (FTC), and Fault Location Prediction (FLP) directly from synchronized raw three-phase current and voltage samples. By bypassing the need for feature extraction, HDLA significantly reduces computational complexity while achieving superior performance compared to existing deep learning methods. The efficacy of HDLA is validated on a comprehensive dataset encompassing various fault scenarios with diverse types, locations, resistances, inception angles, and noise levels. The results demonstrate significant improvements in accuracy, recall, precision, and F1-score metrics for classification, and Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for prediction, showcasing the effectiveness of HDLA for real-time fault diagnosis in power systems.

Reliability assessment of generation capacity in modern power systems via analytical methodologies

With the recent transition to a low-carbon electrical power system (EPS), the large-scale utilization of renewable energy resources in electrical power generation introduces a substantial amount of uncertainty on the generation side of the EPS. This uncertainty, along with the inherent uncertainty of electricity demand, makes assessing generation reliability a very computationally intensive process. To enhance the computation efficiency of EPS generation reliability assessment, it is crucial to have an efficient probabilistic model of available generation capacities that strikes a balance between improved computational performance and model accuracy. In this paper, various probabilistic models are proposed to characterize the variability and uncertainty of conventional and renewable power generations (photovoltaic and wind). On the basis of these models, an analytical formulation of probabilistic reliability indices (RIs) is implemented. The computation time and accurate RIs values found using the Monte Carlo simulation method serve as the basis for reporting solving time improvements with corresponding losses in the accuracy of the RIs for different analytical methodologies. The results of multiple case studies of an EPS are presented, considering various combinations of conventional and renewable generation capacity, levels of renewable power penetration, and system reliability levels. The results indicate the practical implementation of analytical assessment methodologies compared to the simulation method in terms of accuracy and computational effort. This study is of immediate relevance and potential importance to operational reliability and generation expansion planning studies in EPSs.

Transient Synchronous Stability Analysis of Grid-Following Converter Considering Outer-Loop Control with Current Limiting

With the evolution of modern power systems, inverter-based resources have become increasingly prevalent. As critical energy conversion interfaces, grid-following converters exhibit dynamic performances, presenting challenges for system security and stability. This paper focuses on the transient synchronization stability of converters after disturbances, highlighting differences in mechanisms compared to synchronous generators. Although previous studies on the transient synchronization stability of converters have been conducted, they primarily concentrate on the dynamics of the phase-locked loop, with limited consideration of the effects of outer-loop control. This has created a cognitive bottleneck in understanding the transient synchronization mechanisms of converters. To address these challenges, this paper models a grid-following voltage source converter system, incorporating detailed converter control strategies and current-limiting control. The stability regions of the stable equilibrium point under various fault severities are first analyzed. Then, the impacts of outer-loop control, including PI control and current-limiting control, on transient synchronization are examined. The study systematically elucidates the influence of outer-loop control on the transient synchronization stability of converters. Finally, the validity of the proposed theory is confirmed through simulations conducted in PSCAD/EMTDC.