Power System Analysis Research Articles

A Static Security Region Analysis of New Power Systems Based on Improved Stochastic–Batch Gradient Pile Descent

The uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system static stability analysis. To address the slow training speed of traditional deep learning algorithms using batch gradient descent (BGD), we introduce an improved stochastic–batch gradient descent (S-BGD) search method that combines the advantages of stochastic gradient descent (SGD) in fast training. This method ensures both speed and precision in parameter training. Moreover, to tackle the problem of getting trapped in local optima and saddle points during parameter training, we draw inspiration from kinematic theory and propose a gradient pile (GP) training method. By utilizing accumulated gradients as parameter corrections, this method effectively avoids getting stuck in local optima and saddle points, thereby enhancing precision. Finally, we apply the proposed methods to construct the static security region for the IEEE-118 new power system using its data as samples, demonstrating the effectiveness of our approach.

Finite-Time Adaptive Event-Triggered Control for Full States Constrained FONSs with Uncertain Parameters and Disturbances

This article focuses the event-triggered adaptive finite-time control scheme for the states constrained fractional-order nonlinear systems (FONSs) under uncertain parameters and external disturbances. The backstepping scheme is employed to construct the finite-time controller via a series of barrier Lyapunov function (BLF) to solve that all the state constraints are not violated. Different from the trigger condition with fixed value, the event-triggered strategy is applied to overcome the communication burden of controller caused by the limited communication resources. By utilizing fractional-order Lyapunov analysis, all variables in the resulted system are proven to be bounded, and the tracking error converges to the small neighborhood around origin in finite time and without the Zeno behavior. Finally, the effectiveness of the proposed control scheme is verified by the simulation analysis of a bus power system.

An Efficient Methodology to Identify Relevant Multiple Contingencies and Their Probability for Long-Term Resilience Studies

The selection of multiple contingency scenarios is a key task to perform resilience-oriented long-term planning analyses. However, the identification of relevant multiple contingencies may easily lead to combinatorial explosion issues, even for relatively small systems. This paper proposes an effective methodology for the identification of relevant multiple contingencies and their probabilities, suitable for the long-term resilience analysis of large power systems. The methodology is composed of two main pillars: (1) the clustering of lines that are more likely to fail together, to reduce the computational complexity of the analysis exploiting historical weather data and (2) the probability-based identification of multiple contingencies within each cluster, where the contingency probability is computed applying the copula theory. Tests performed on a portion of the Italian EHV transmission system confirm the validity of the clustering results compared against historical failure events. Moreover, the copula-based algorithm for contingency probability estimation passes the tests carried out on relatively large clusters with very low error tolerance. The method successfully pinpoints critical multiple contingency scenarios and their likelihoods, making it valuable for assessing power system resilience over long-term horizons in support of resilience-oriented planning activities.

ST-AGNet: Dynamic power system state prediction with spatial–temporal attention graph-based network

Accurate and timely prediction of power system states is one of the most important challenging tasks in modern power systems. Considering the integration of renewable energy sources, recent deep learning-based models have been well studied and found to have benefits in exploiting spatial–temporal relationships in power system data. However, the complexity of different power system topology structures is not substantially captured since the existing models did not fully consider the graph-based information retrieved from power networks. To resolve the problem, a spatial–temporal attention graph-based network (ST-AGNet), an adaptive power system state prediction approach that utilizes graph-based information data to account various typologies of complex power systems, is proposed. Initially, the power flow model is used for generating historical system state data. With the graph-based topology information, the input dataset with the spatial and temporal features is fed into the proposed network for the training and validating process. Meanwhile, the connectivity of the time-varying graph-based information are accounted in the proposed model. Case studies demonstrate the superiority of the ST-AGNet model over the existing baselines under four different scales of complex systems, which can significantly support dynamic power system analysis and operational tasks.

Design of an Intelligent Energy Management System for Electric Vehicle Charging Station

In today’s world there are more outcomes in environmental change due to the overutilization of petroleum products in this manner prompting a genuine effect on the climate. So there is a need for a substitute answer for lessen the consumption of such non – sustainable assets. One such exertion made in the field of Freeways is the advancement of "Solar Freeways" which can be an elective arrangement. Sun oriented streets consolidate various arrangements in one – it can assist us with improving the creation of power utilizing sun based boards, to give a computerized stage to our future country's ventures like Smart Cities, and to work with the arising electric vehicles that supplant the petroleum driven vehicles and substantially more. Motivated by the fact that there are numerous amount of clean and sustainable energy we receive from roadways, the following study puts forward some of the event and application of an innovative charging method for the renewable energy driven electric cars, buses by using the roadway and also implementation of revolutionary nanotechnology along with the latest best in the house power electronics and power system analysis tools. A small scale prototype model was made by our team to attest the working of smart inductive charging process. Our project team was successful to improve the working of the model by improving the use of the preinstalled solar panels and also implement its use on the very concept we are trying to improve. On the vehicle, there will be the use of coils which are experimentally made for the flow of charges that are needed to provide charge to a moving electric vehicle (EV). The detailed strategy is presented in this report. Key Words: Environmental change, Overutilization of petroleum products, Solar Freeways, Renewable energy, Electric vehicles (EVs), Smart Cities, Nanotechnology

Techno-Economic and Environmental Analysis of a Hybrid Power System Formed From Solid Oxide Fuel Cell, Gas Turbine, and Organic Rankine Cycle

Abstract Distributed energy technology is an essential pathway for future advancements in the field of energy technology. In the present study, organic Rankine cycle (ORC) is integrated with solid oxide fuel cell (SOFC)-gas turbine (GT) hybrid power system. The conventional metrics employed for assessing the performance of SOFCs, gas turbines, and organic Rankine cycles, such as voltage and gross real efficiencies, have some limitations as indices of merit. Contemporary second law concepts and economic and environmental analysis have been used to enhance hybrid power system evaluation. R1233zd(E) has been selected as the ORC working fluid. The outcomes reveal that, under certain conditions, the present configuration may reach 55.67% energy efficiency and 53.55% exergy efficiency. Economic and environmental analysis shows that the hybrid system's total cost rate and Emissions of CO2 gas (EMI) under design conditions are 36.09 $/h and 355.8 kg/MWh, respectively. Thermodynamic evaluation of present SOFC-GT-ORC configuration shows 11.72% improvement in exergy efficiency compared to hybrid SOFC-GT cycle. Consequently, the hybrid SOFC-GT-ORC system is far better than the hybrid SOFC-GT system. In the future, other ORC fluids like R123, R601a, and R245fa can be used as ORC fluids.

Large scale penetration impact of PMS-WTGS on voltage profile and power loss for 5-bus, 330kV system of the Nigerian grid

In recent times, adverse environmental changes and the gradual reduction in fossil fuel deposits is making integration of renewable energy secure global attention. This paper therefore investigates the effect of high penetration of wind power on power loss and voltage profile of 52-bus, 330kV Nigerian grid. Modeling and simulation of the study were successfully done using the Continuation Power Flow method and Power System Analysis Toolbox in the Matrix Laboratory environment. The Ant Colony Optimization (ACO) technique codes were used to determine the best sites and sizes of Permanent Magnet Synchronous-based Wind Turbine Generators (PMS-WTGs) on the grid as nonoptimal placement and size of PMS-WTGs will result into active power losses and poor voltage profile. The ACO and power flow analysis indicated two load buses, Aladja bus and Yola bus as best sites for PMS-WTGs with optimal sizes of 100MW and 197MW respectively. The result shows a decrease in the system's active (42.1%) and reactive (43.9%) power losses while heightening voltage magnitudes of critical buses to statutory values and enhancing the grid power quality. The overall results indicate that integrating 297MW PMS-WTGs improves voltage stability and power loss reduction by maximizing system loadability and reducing line losses.

Automatic processing of dynamic responses via wavelet transform for the development of dynamic equivalent models

The ever-increasing requirements for electricity, the emergence of microgrids and the escalating penetration of distributed generators has reinvigorated the interest in dynamic equivalencing of distribution networks, due to its significance in power system analysis. Scope of this paper is to propose an efficient method for event detection and filtering of dynamic responses based on the wavelet transform (WT), in order to improve the quality of signals used for the derivation of dynamic equivalent models. The accuracy of the proposed method is tested using artificially created noisy responses, by applying Gaussian, Laplace, and Student’s-t noise distributions. Comparisons with other filtering techniques are also performed and the impact of all methods on the derivation of accurate equivalent model parameters is quantified and analyzed. The performance of the proposed method is also evaluated by using RMS responses obtained from a large-scale distribution network model as well as by analyzing laboratory measurements; results verify the efficiency and applicability of the WT-based processing procedure, by achieving a parameter estimation error well below 1%. It is noteworthy that the average computational burden throughout the process remains under 0.39 s.

Mixed Algorithm of SINDy and HAVOK for Measure-Based Analysis of Power System with Inverter-based Resource

Artificial intelligence and machine learning is enhancing electric grids by offering data analysis tools that can be used to operate the power grid more reliably. However, the complex nonlinear dynamics, particularly when coupled with multi-scale interactions among Inverter-based renewable energy Resources, calls for effective algorithms for power system application. This paper presents affective novel algorithm to detect various nonlinear dynamics, which is built upon: the Sparse Identification of Nonlinear Dynamics method for nonlinear dynamics detection; and Hankel Alternative View of Koopman method for multi-scale decomposition. We show that, by an appropriate integration of the strengths of the two, the mixed algorithm not only can detect the nonlinearity, but also it distinguishes the nonlinearity caused by coupled Inverter-based resources from the more familiar ones caused synchronous generators. This shows that the proposal algorithm can be a promising application of artificial intelligence and machine learning for data measure-based analysis to support operation of power system with integrated renewables.

Processing of PMU Data Acquisition for Operational Power System Analysis

Processing of PMU Data Acquisition for Operational Power System Analysis

Stability Analysis in a Direct-Current Shipboard Power System with Parallel Permanent Magnet Synchronous Generators and Supercapacitor Integration

This paper investigates the small-signal stability of a DC shipboard power system (SPS) with the integration of a supercapacitor. As an efficient energy storage solution, supercapacitors can not only provide rapid energy response to sudden power demand spikes, effectively mitigating load fluctuations, but also enhance the system’s resilience to disturbances. In the context of the parallel operation of two Permanent Magnet Synchronous Generators (PMSGs), the inclusion of supercapacitors may alter the system’s dynamic behaviors, thereby affecting its small-signal stability. This paper develops the small-signal model of SPS and explores the small-signal model under various power distribution strategies in the parallel operation of diesel generator sets. Through the calculation of eigenvalues and influence factors, the system’s oscillation modes are analyzed, and key parameters affecting the stability of the DC distribution system are identified. Furthermore, this paper meticulously examines the specific impacts of electrical and control parameter variations on the system’s small-signal stability. Simulation experiments validate the accuracy of the small-signal stability analysis after supercapacitor integration into SPS.

Short‐term electric load forecasting based on empirical wavelet transform and temporal convolutional network

AbstractShort‐term load forecasting is the basis of power system operation and analysis and is of great significance for the stable operation of power systems. To solve the problems of insufficient mining of the intrinsic information of load data, randomness, and non‐linearity, and to improve the accuracy of load prediction, the authors propose a short‐term power load prediction model based on empirical wavelet transform (EWT) decomposition and the temporal convolutional network (TCN). First, the Pearson coefficient is used to eliminate the less relevant influencing factors to reduce the complexity of the model. Then, the EWT algorithm is used to decompose the original load signal to fully exploit the load data time domain and frequency domain information, while solving the problems of non‐linearity and randomness. Finally, the decomposed subsequences are input to the TCN network for prediction superposition to obtain the final results. The experimental results show that, compared with the single models TCN, gated recurrent units (GRU), and long short‐term memory (LSTM), and the hybrid models EWT‐GRU, EWT‐LSTM, and VMD‐TCN, the R2 of the short‐term power load forecasting model based on EWT‐TCN is improved by 0.2099%, 0.2519%, 0.3453%, 0.0334%, 0.1766%, and 0.1228%, respectively.

Enhanced complete ensemble EMD with superior noise handling capabilities: A robust signal decomposition method for power systems analysis

AbstractSignal decomposition is crucial in several domains, particularly in the dissection of complex signals present in electrical power systems. Understanding the oscillations and patterns within these signals can significantly influence energy resource management, grid stability, and efficient system operation. This paper presents an advanced enhanced decomposition method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to mitigate the inherent drawbacks of the conventional CEEMDAN and its improved version. Unlike CEEMDAN's generalized noise approach, the proposed method introduces adaptive noise, enhancing target signal noise handling by incorporating a tailored filtering and updating process after each iteration. This leads to more accurate signal decomposition compared to traditional methods. Comprehensive tests were conducted using artificially generated signals characterized by mode mixing, varying frequency oscillations, complex real‐world electrical demand signals, generator axis vibrations and partial discharge signals. The results demonstrate that the proposed method outperforms traditional techniques in two significant aspects. First, it provides superior spectral separation of the intrinsic modes (IMF) of the signal, thereby enhancing decomposition accuracy. Second, it significantly reduced the number of shifting iterations, thereby alleviating the computational load. These advancements have led to a more accurate and efficient framework that is essential for analyzing nonlinear and nonstationary signals.

Design and performance analysis of hydro and wind-based power and hydrogen generation system for sustainable development

Renewable energy sources have significance in combating global challenges. In this developed paper, the integration of the hydropower and wind turbine energy conversion system is designed and proposed. The foremost focus of this combined plant is to simultaneously generate clean power, hydrogen, and freshwater through a sustainable way. The developed innovative plant embraced a hydropower plant, wind turbines, a Proton Exchange Membrane unit, and a Reverse osmosis desalination unit. To determine the system performance, detailed thermodynamic performance modeling and sustainability index evaluation are fulfilled on the plant’s indicators. In light of this purpose, several parameters are used to conduct the irreversibility rate, energetic and exergetic efficiency, and other calculations. Along with the examination results, the electricity production rate is calculated as 81591 kW. Moreover, the developed combined system has 61.29 % energetic performance and 57.14 % exergetic performance. The sustainability index value is figured out as 2.33 for the entire system.

Spectro-angular analysis of roadside-integrated bifacial solar power systems with reflecting sound barriers

Bifacial photovoltaic modules along highways provide energy supply and act as sound barriers simultaneously. This study examines the impact on energy production when incorporating sound barriers with varying light reflection properties into this integrated solar infrastructure along roadways. Specifically, we use advanced computational simulations to analyze the effects of integrating black, ideal specular, and ideal diffuse (Lambertian) reflectors into an existing highway solar power plant located in the Netherlands. Our analysis combines realistic spectro-angular irradiance data as input with our in-house reverse ray tracing software. Our calculations show that for an east-west facing system, an ideal diffuse reflector increases the annual yield by 70%, while a specular reflector decreases the yield due to shading. Most notably, the diffuse reflector doubles the energy yield during winter months, thereby offering a pathway to decrease the seasonal energy demand and supply gap.

Protection System of the Electric Power Distribution System of Notre Dame of Marbel University, Koronadal City, South Cotabato

A vital instrument for evaluating the effectiveness and safety of electrical systems is the power system analysis. Protecting people and equipment from errors is part of this. These assessments examine the load, the equipment, and the foreseeable needs. A lack of short-circuit current protection can harm equipment and endanger workers. The main goal of this study is to assess if the various protection devices utilized in the Notre Dame of Marbel University’s Main Campus electrical system comply with the established standards. Through a real survey (opening panel boxes and reviewing the original Electrical Plan), the researcher will ascertain the various protection devices in operation. Two approaches are used to test for short circuits: the MVA method and the per-unit method. When determining fault current, only the three-phase fault is considered in the calculation since it is the one having the largest fault value. Aside from the mentioned, the transformer rating calculations were also done. The resulting data will then be compared to the actual protective devices installed in the building. Short circuit currents must be less than the Kilo Ampere Interrupting Capacity rating of the installed protective devices. In conclusion, after conducting a thorough analysis of the current loading schedule and comparing the results obtained through the MVA and per-unit methods, it is evident that the various installed Overcurrent Protective Devices are operating safely. The calculations revealed a substantial disparity of 35.57% between the two methods. Furthermore, the t-test conducted on paired data confirmed the statistical significance of the differences between these two approaches. With this, the researcher recommends conducting a separate study to verify branch circuit calculations, implementing primary metering, and prioritizing the rehabilitation of the Pantua and Alunan lines to enhance the safety and reliability of the electrical system, particularly in tracing complex branch circuits. Keywords: power system analysis, MVA method, per unit method, comparative analysis, Notre Dame of Marbel University

Optimal Sizing and Techno-Economic Analysis of a Hybrid Power System for Postville

Providing reliable and sustainable electricity to remote communities poses a significant challenge. Techno-economic feasibility of an off-grid hybrid renewable energy system for Postville, a remote Northern Labrador, Canada community, is presented in this paper. The study integrates solar photovoltaic panels, wind turbines, battery storage, and diesel generators into a hybrid system, analyzing various components and optimizing using advanced Homer Pro software. Simulation results demonstrate that the most optimized hybrid structure ensures a stable power supply while minimizing diesel generator operation, reducing fuel consumption. Economically, this system offers substantial cost savings, alleviating the financial burden on the community. The designed system consists of 435 kW PV, 500 kW wind turbine, 455 kW diesel generator, and 815 kWh batteries. The proposed system will generate power with a net present cost of $5.57 million. This research also contributes to the broader goal of creating sustainable energy solutions and improving the quality of life in remote communities.

Power Systems and Power Electronics using the DEMATEL Method

Energy and research on energy systems places a focus on all facets of electrical energy, as well as innovation in energy production and delivery, alternative resources, and efficient devices. Systems and equipment for converting, providing, and utilising energy as a kind of electricity are the subject of research initiatives. Power electronics are now a more integral part of power systems, enhancing quality and efficiency and fostering the gradual materialization of intelligent, efficient energy. Many different types of power electronics exist in power systems. The architectural study of changing electrical transformed from one medium to another is known as power electronics. Fields such as electronics reprocess or recover more than 80percent of the entire of the electricity produced at a world average rate of 3.4 billion kilowatts per hour each year. Power electronics converters, sometimes referred to as power converters or switching converters, are used to process or convert electrical energy. Electricity comes in two flavours: AC power and DC power. The distribution system is divided into AC distribution systems and DC distribution systems based on the type of power it uses. Power system analysis is an essential part of electrical power system design. Calculations and simulations are performed to verify that the electrical system, including the system components, is properly specified to perform as planned, withstand the expected stress, and be protected from failures. Advantages of Power Electronics: High power density electricity. Improved efficiency up to 99% in energy conversion. Because to its efficiency and dependability, switching power supply are also being used in medical devices with acoustically sensitive applications. Power system reliability, in general, relates to problems like service interruption and power outages. This is frequently described as a goal to try relying on codes specifically relevant to the consumer. SAIFI, SAIDI, and CAIDI are common dependability index values for US applications. DEMATEL (Decision Making Trial and Evaluation Laboratory) They are divided into analysis using the Nonmetal mineral product industry, General equipment manufacturing, Mining and washing of coal, Textile industry, Food manufacturing industry It is the interaction between the factors Visualized and assesses dependent relationships Through the structural model Also deals with identifying important. Power Electronics in Power Systems, Power Electronics in Transportation Systems, Energy Conservation, Heating & Lighting Control and Renewable Energy Integration Power Systems and Power Electronics in Power Electronics in Power Systems is got the first rank whereas is the Energy Conservation is having the Lowest rank. Power Systems and Power Electronics in Power Electronics in Power Systems is got the first rank whereas is the Energy Conservation is having the Lowest rank.

Application of SSSC for Voltage Stability Improvement in the Nigerian 330 kV Transmission System using Particle Swarm Optimization Technique

Load imbalance has always been a major cause of voltage instability of the power system network and increase in voltage instability affects the power system reliability. In this paper, static synchronous series capacitor (SSSC) FACTS device was implemented to improve the voltage stability in the Nigerian 330 kV transmission network using particle swarm optimization (PSO) technique. Modelled power system network in the power system analysis toolbox (PSAT) is simulated to determine the existing power flow analysis and the level of instability in the power system generation plants situated in the Northern region. The outcome of the power flow is thereafter, optimized with PSO by generating the relationship with a high prediction accuracy between voltage at each location and an inter-linked transmission line distance of the 330kV. The model was generated with least square method and the high prediction accuracy determination was carried out with R-square value of the polynomial order. The best model was optimized and the entire computations were carried out in MatLab resulting in the determination of the optimal location which was between Jebba transmission and Shiroro generation stations. SSSC FACTS controller was implemented on the optimized transmission line and the simulation results showed that the introduction of SSSC improved the voltage stability of the power system network.

Guest Editorial: Dynamic analysis, control, and situation awareness of power systems with high penetrations of power electronic converters

Guest Editorial: Dynamic analysis, control, and situation awareness of power systems with high penetrations of power electronic converters