Power Integration Research Articles

Hierarchical Blocking Control for Mitigating Cascading Failures in Power Systems with Wind Power Integration

The increasing uncertainty of wind power brings greater challenges to the control for mitigation of cascading failures. In order to minimize the risk of cascading failures in large-scale wind power systems at a lower economic cost, a multi-stage blocking control model is proposed based on sensitivity analysis. Firstly, the propagation mechanism of cascading failures in power systems with wind power integration is analyzed, and the propagation path of such failures is predicted. Subsequently, sensitive lines that are prone to failure are identified using the power sensitivity matrix, taking into account the effects of blocking control on the propagation path. By constraining the power flow of these sensitive lines, a multi-stage blocking control model for the predicted cascading failure path is proposed with the objective of minimizing the control cost and cascading failure probability. Based on probabilistic optimal power flow calculations, the constraints related to wind power uncertainty are transformed into opportunity constraints. To validate the effectiveness of the proposed model, the IEEE 39-node system is used as an example, and the results show that the obtained control method is able to balance economy and safety. In addition, the control costs for the same initial failure are higher as the wind power penetration rates and confidence levels increase.

Assessment of High Wind Power Permeability Power System Frequency Regulation

Global wind power has expanded quickly in recent years to help with the energy issue and environmental degradation. The large-scale integration of wind power has become a hot topic for academic research in recent years due to the potential to improve the stability of the power system through the application of wind power participation in power grid frequency control. This study reviews the research on power system frequency regulation approach with high wind power permeability and classes the frequency control difficulties of wind power integration. A review and analysis are conducted on the energy storage system that is involved in the frequency management of the power system with high wind permeability.

Reduced stage three phase multisource hybrid converter for integration of PV and grid power

A three-phase-based Modified Dual Source Inverter has been proposed in this manuscript. The existing three-phase system is plagued with multistage power conversion, reducing its efficiency. The proposed converter can integrate two sources (AC and DC) simultaneously and supplies power to the three-phase load. It can switch its operation from the grid to grid-solar, solar to grid, as per the availability of the source. The inverter can operate as a three-phase DC to AC inverter, AC to AC inverter, and it can use grid and PV power directly with the help of a sino-squo-sino waveform. The system has been studied when switching from one mode to another. The system behavior has been analyzed on MATLAB SIMULINK and experimental setup. The proposed approach has been experimentally validated and operated using a TEXAS-based microcontroller (TMS320F28739d). The proposed method has reduced the THD to 4.32% when operated on solar and grid. The THD is 0.25% when operated on the grid power. The proposed system has also been compared based on components. It uses six IGBTs, One Mosfet and three Bidirectional switches.The reduced number of components reduces switching losses, which increases the system's efficacy and performance.

Ultra-short-term wind power forecasting techniques: comparative analysis and future trends

In recent years, the integration of wind power into the grid has steadily increased, but the volatility and uncertainty of wind power pose significant challenges to grid planning, scheduling and operation. Ultra-short term wind power forecasting technology as the basis of daily scheduling decision can accurately predict the future hourly wind power output, and has important research significance for ensuring the safe and stable operation of power grid. Although research on ultra-short-term wind power forecasting technology has reached maturity, practical engineering applications still face several challenges. These challenges include the limited potential for improving the accuracy of numerical weather forecasts, the issue of missing historical data from new wind farms, and the need to achieve accurate power prediction under extreme weather scenarios. Therefore, this paper aims to critically review the current proposed ultra-short-term wind power forecasting methods. On this basis, analyze the combined power forecasting method under extreme weather scenarios, and illustrate its effectiveness through wind farm case studies. Finally, according to the development trend and demand of future power systems, future research directions are proposed.

DC Microgrid for Robotic Manufacturing – field demonstration and laboratory experience.

Paper present real manufacturing application based approach to DC based power supply and infrastructure verification. Alternative solutions for improved management of power flows and integration of renewable energy sources has become an interesting topic and discussions of alternative DC related power infrastructure with respect to existing technology. Since modern manufacturing solutions implement industrial robot manipulators more detailed evaluation of possible opportunities regarding DC based supply has been introduced. Typical distribution of units and respective electrical power flows present need for power measurement equipment with multipoint functionality experiments. Obtained practical measurement results enable evaluation of DC based energy distribution system potential compared to existing solution and future operation scenarios including energy storage and buffering approaches. Implementation of single AC/DC converter unit has been evaluated in laboratory conditions with respect to power quality within DC distribution link.

Study of deployment of Pre-Insertion resistor to disconnector for mitigating overvoltage on 525 kV current transformers

Because of the incidence of voltage pulse trains generated from intrinsic arcs existing during the operation of adjacent disconnectors, several failures in 550 kV class current transformers have been reported across the Brazilian integrated power grid. Based on previous correlated research, the authors present in this article computational results of their proposition in adopting the pre-insertion resistor-fitted disconnector type for addressing this issue, which is already indicated to be adopted in power substations with 800 kV or higher although for different reasons. With this aim, ATP modeling has been developed for analyzing a study case of a section of a bay of a 550 kV power substation. The obtained results indicate not only the drastic reduction in the overvoltage amplitude, in about 18 times less for the value of the Severity Factor, as well as in the significant reduction in both to one-third in the rate of the overvoltage rise and in the maximum amplitude of the current train pulses, which show this proposal to be technically promising. Yet, a study of the influence of the value of the pre-insertion resistor and the respectively adopted configuration is presented, based on what is commercially available in view of making this proposal to be economically viable.

Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

The constant rise in energy demand and concerns about climate change have led to increased penetration of renewable energy sources (RES). Maintaining active power balance between generation and demand in power systems with significant penetration of these highly variable and intermittent renewable sources requires an efficient load frequency control (LFC) strategy. One such strategy that has gained the attention of researchers is optimal tuning of PID controllers of LFC using metaheuristic method. This paper presents a PSO variant for optimal tuning of PID controllers for load frequency control of power system integrated with renewable energy resources. The proposed PID tuning technique is tested on a two-area power system commonly used in the literature. Seven scenarios have been used to validate the effectiveness of the proposed Load Frequency Control. For more realistic evaluation, governor dead band and communication time delays have been incorporated in the test system in one of the scenarios. Simulation results obtained when compared with those of three well-known PID-tuning metaheuristic algorithms produced shorter settling time and smaller frequency and tie line power deviations.

Experimental Assessment of a Decentralized Control Strategy for a Back-to-Back Modular Multilevel Converter Operating in Low-Frequency AC Transmission

The Modular Multilevel Converter (MMC) has been widely used in high-power applications owing to its inherent advantages, including scalability, modularity, high-power density, and fault tolerance. MMCs have recently been used in Low-Frequency Alternating Current (LFAC) transmission, particularly in the integration of offshore wind power with onshore grids. However, LFAC applications produce significant voltage oscillations in floating capacitor voltages within the MMC. Early research efforts have successfully established and validated decoupled control strategies for LFAC-based MMC systems. However, validations are usually based on simulations or small-scale prototypes equipped with limited power cells. Consequently, this paper presents a decentralized voltage control strategy based on Nearest Level Control for an MMC-based LFAC system. Experimental results obtained with a 120-cell MMC prototype are presented to validate the effectiveness and operation of the MMC in LFAC applications.

An optimal membership function based Fuzzy-[formula omitted] stabilizer design to suppress low frequency oscillation in synchronous generator

An optimal membership function-based fractional order fuzzy proportional integral (Fuzzy-PIλ) power system stabilizer (PSS) is designed to suppress low frequency oscillation (LFO) in a synchronous generator connected to an infinite bus power system. Speed deviation and the change in speed deviation of synchronous generator due to LFO are taken as inputs of the fuzzy controller, and the parameters of a fractional-order PI controller (proportional gain, integral gain, and the fractional term in integral control action) are taken as fuzzy output variables. The range, scaling, and shape of the membership functions of the fuzzy controller are optimized such that it can tune the fractional order proportional integral (PIλ) stabilizer under any disturbance or parametric uncertainty in the synchronous generator. The advantage of the proposed stabilizer is that it can handle a wide range of disturbances and adjust the gains of the control parameters for the system. A nonlinear dynamic acceleration coefficient-based class-topper optimization is applied to optimize the fuzzy settings. The robustness and sensitivity of the proposed optimal Fuzzy-PIλ stabilizer are verified under different scenarios for the synchronous generator and compared with available results.

Active Power Allocation Method of Doubly Fed Induction Generators Based on Rotor Speed

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia support under maximum power point tracking control. To make the wind turbine improve the inertia support ability of the system based on virtual inertia control, a method for active power allocation based on the rotor speed of DFIG is proposed. Firstly, the minimum system inertia requirement based on the frequency change rate of the system is established. Active power allocation assumes that the wind farm inertia meets the minimum system inertia requirement. Secondly, the objective is to enhance the inertia support capability and overall active power output of the wind farm, considering the constraint of the minimum system inertia requirement. Based on the rotor speed to establish the inertia allocation weight factor, the weight of the power command is assigned to a single machine to achieve the wind farm active power allocation. Finally, it is verified that the system’s equivalent inertia meets the minimum inertia requirement of the system. Simulations show that the proposed allocation method can adequately elevate the inertia support capability of DFIGs to the system and the rotor kinetic energy utilization.

Power of Internal Systems Integration: A Pathway to Organizational Performance for Clearing and Forwarding Firms in Kenya

Power of Internal Systems Integration: A Pathway to Organizational Performance for Clearing and Forwarding Firms in Kenya

Wind power integration improvement in an island by installing battery energy storage systems. Case study of Lanzarote-Fuerteventura.

Some small isolated power systems have a grid infrastructure weakly meshed and stability could be seriously affected when a large amount of renewable energy is being flowed into the grid. In this paper critical clearing time (CCT) is studied along with the increase in renewable energies integration. This study considers the use of squirrel cage induction generators (SCIG) wind turbines. Battery energy storage systems (BESS) have been considered in order to overcome this kind of power system drawbacks. Thus, renewable energy integration improvement is achieved. Renewable energy improvement is analyzed by means of CCT values. Dynamic analysis is performed in order to determine CCT values taking into account voltage, system frequency and the amount of load shedding.

Day-ahead bidding and dispatch strategy of novel battery charging and swapping station under multiple uncertainties

The novel battery charging and swapping station (NBCSS) has great operational flexibility due to its integration of wind power, photovoltaic power, gas turbine and energy storage. This paper presents a day-ahead bidding and dispatch strategy of NBCSS under multiple uncertainties, which consists of battery demand, market price, renewable energy and load. Firstly, the optimized backup method is used to address the uncertainty of battery demand. Then, a two-stage stochastic robust optimization model is further applied to address the uncertainties of market price, renewable energy and load. The model’s goal is to minimize the total operational cost in the worst-case scenario, which is limited by the corresponding uncertainty set. Especially, to control the conservatism of the model, the total adjustable budget and temporal adjustable budget of uncertainties are considered together. Next, the two-stage model is solved efficiently based on the strong duality theory and column-and-constraint generation (C&CG) method. Finally, case studies are given to verify the effectiveness and performance of the proposed model. The results demonstrate that the NBCSS can be efficiently deployed, enabling market arbitrage and reducing the overall cost. Additionally, the conservatism of the proposed model can be adjusted according to the budgets of uncertainties.

Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

Exploring the Rural Revitalization Effect under the Interaction of Agro-Tourism Integration and Tourism-Driven Poverty Reduction: Empirical Evidence for China

Under the robust impetus of China’s rural revitalization strategy, agro-tourism integration and tourism-driven poverty reduction have profoundly impacted various aspects of China’s economy, society, and ecology. This has propelled coordinated urban–rural development and the sustainable development of the tourism industry. This study introduces an analytical framework encompassing tourism-driven poverty reduction, agro-tourism integration, and rural revitalization. Through PVAR and threshold models, it empirically examines the interactive effects, dynamic relationships, and threshold effects between agro-tourism integration, tourism-driven poverty reduction, and rural revitalization. The conclusions are as follows: Firstly, the indices of rural revitalization and the level of agro-tourism integration show an increasing trend across Chinese provinces, with varying trends in tourism-driven poverty reduction efficiency. Secondly, there is a significant dynamic relationship among these factors, with the explanatory power of tourism-driven poverty reduction and agro-tourism integration gradually strengthening. Agro-tourism integration is identified as the most effective means of driving rural revitalization, while tourism-driven poverty reduction has a relatively weaker direct impact. Thirdly, tourism-driven poverty reduction exhibits a positive impulse response to agro-tourism integration. The improvement in tourism-driven poverty reduction efficiency propels further development in agro-tourism integration, thereby fostering rural revitalization. The efficiency of tourism-driven poverty reduction presents a single threshold effect in the process of agro-tourism integration promoting rural revitalization. Fourthly, the development of China’s tourism industry has become an indispensable means of promoting rural revitalization and poverty reduction. However, rural revitalization is a comprehensive project influenced by various factors, requiring improvements and development across multiple aspects.

Carbon emission reduction contribution analysis of electricity enterprises in urban green development: A quantum spherical fuzzy sets-based decision framework

As managers of urban power grids (UPGs), electricity enterprises are responsible for ensuring energy security and stability during the urban green transformation process. They are also a key driving force in promoting green power transmission and integration. Therefore, exploring the green attributes of UPGs and accurately enhancing their green support capabilities are of significant importance. This study addresses the perceived contributions of electricity enterprises to carbon emission reduction and proposes a decision framework within a quantum-spherical fuzzy (QSF) environment. First, from the perspectives of economy, technology, policy, and management, a carbon emission reduction contribution evaluation system for electric enterprises was constructed. In the QSF environment, this study identifies the importance of various attributes in the evaluation system. Subsequently, an electric enterprise carbon emission reduction perception model was developed based on the preference ranking organization method for enrichment evaluations and prospect theory (PROMETHEE-PT). An empirical validation of the proposed model was conducted in Hefei, China, demonstrating the robustness and effectiveness of the evaluation framework. The study's results were verified by comparing them with those of other methods.

Machine Learning Based Data Validation for Signal Integrity and Power Integrity Using Supervised Time Series Classification

Machine Learning Based Data Validation for Signal Integrity and Power Integrity Using Supervised Time Series Classification

Join Us in May 2025 for the 29th IEEE Workshop on Signal and Power Integrity, to be Held in Gaeta, Italy!

Join Us in May 2025 for the 29th IEEE Workshop on Signal and Power Integrity, to be Held in Gaeta, Italy!

Signal Integrity and Power Integrity

Signal Integrity and Power Integrity

Up for the challenge: Power motive congruence drives nurses to craft their jobs and experience well-being.

Job crafting is the behavior that employees engage in to create personally better fitting work environments, for example, by increasing challenging job demands. To better understand the driving forces behind employees' engagement in job crafting, we investigated implicit and explicit power motives. While implicit motives tend to operate at the unconscious, explicit motives operate at the unconscious level. We focused on power motives, as power is an agentic motive characterized by the need to influence your environment. Although power is relevant to job crafting in its entirety, in this study, we link it to increasing challenging job demands due to its relevance to job control, which falls under the umbrella of power. Using a cross-sectional design, we collected survey data from a sample of Lebanese nurses (N = 360) working in 18 different hospitals across the country. In both implicit and explicit power motive measures, we focused on integrative power that enable people to stay calm and integrate opposition. The results showed that explicit power predicted job crafting (H1) and that implicit power amplified this effect (H2). Furthermore, job crafting mediated the relationship between congruently high power motives and positive work-related outcomes (H3) that were interrelated (H4). Our findings unravel the driving forces behind one of the most important dimensions of job crafting and extend the benefits of motive congruence to work-related outcomes.