Power System State Research Articles

Consensus-Based Power System State Estimation Algorithm Under Collaborative Attack

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker’s perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out.

Research on condition assessment of nuclear power systems based on fault severity and fault harmfulness

Obtaining real time quantitative condition assessment results is a focus in the field of nuclear power system PHM. The existing researches on the nuclear power system condition assessment based on the Multi-Criteria Decision-Making framework do not consider the faults perniciousness differences. This leads to the assessment results relying on the system status deviation degrees(severity), and the weighting and aggregation processes cannot effectively reflect the differences in the consequences and risks of different faults, which represent the faults harmfulness. A quantitative assessment method based on fault severity and fault harmfulness is proposed to address this issue. The novel method quantifies the fault severity based on the similarity principle while diagnosing system faults, and then analyzes the devices mainly affected by various faults to quantifies the harmfulness. Combining the system status deviation degrees(severity), the devices and parameters importance, and the differences in faults harmfulness, the novel method aggregates comprehensive system condition assessment results. Based on a widely recognized nuclear power system faults dataset, the novel method was compared with other methods. The conclusion is that the novel method considers the differences in the faults harmfulness, resulting in more reasonable assessment results and avoiding insufficient or excessive warnings.

A long-term power system state calculation method using sequential power and holomorphic embedding

Traditional Power Flow (PF) calculations struggle to support the data analysis requirements as the power system develops. Therefore, this paper introduces a non-sequential and non-iterative PF calculation method to enhance the computational speed and analytical efficiency of power system state variables over long time scales. Firstly, a Power Sequential Division (PSD) method is proposed, representing the power curve using hierarchical energy with temporal characteristics. Next, based on the Fast and Flexible Holomorphic Embedding (FFHE) method, the paper explores the incremental properties of Voltage Power Series Coefficients (VPSC) and proposes an Incremental Holomorphic Embedding (IHE) method. Finally, by combining PSD and IHE, this study introduces a Power Sequential Flow (PSPF) method for rapid computation of power system state variables over long time scales. Unlike traditional PF methods, PSPF method shifts away from time-series calculations and instead analyzes the distribution of power system state variables over long time scales from an energy perspective. Various test cases are set up to analyze the performance characteristics of IHE and PSPF from multiple perspectives. The results indicate that the proposed methods significantly outperform traditional PF methods in terms of computational speed, adaptability to test cases, and analytical efficiency.

Application on ac station service system for gathering station based on roof photovoltaic system

Abstract With the increasingly severe global energy crisis and environmental pollution, it is imperative to develop and utilize clean and renewable energy. In order to achieve carbon peak and carbon neutrality as soon as possible, and reduce the proportion of fossil energy generation, decarbonization in the power industry is imperative. This article focuses on the design of rooftop photovoltaic systems at gathering stations. By installing solar photovoltaic panels on the roof of the gathering station building, a photovoltaic power generation system is constructed to convert solar energy radiated into electricity. The DC power generated by the solar photovoltaic power generation system is converted by an inverter and connected to the 380V bus of the station power system as a supplementary power supply for the station load, thereby increasing the proportion of green electricity consumption in the collection station and achieving the goal of carbon reduction

Preface

In the ever-evolving landscape of technological advancements, the fusion of smart energy systems and the Internet of Things has emerged as a pivotal force driving global sustainability and innovation. With this vision in mind, the 2024 3rd International Conference on Smart Energy and Energy Internet of Things (SEEIoT 2024) was successfully convened from June 21st to 23rd, 2024, in Chengdu, China. SEEIoT 2024 is not only a focused display of the latest research results in smart energy systems and the Internet of Things, but also an important platform to promote the development and application of disciplines and domains related to smart energy systems and the Internet of Things globally. It attracted scholars, experts, researchers and engineers from all over the world, and a total of about 100 delegates participated in the event, including some international delegates, which reflects the internationalization and extensiveness of the fields of smart energy systems and the Internet of Things. The event was packed with a diverse and engaging agenda, comprising keynote speeches, oral presentations, poster sessions, and academic discussions. During the conference, participants had in-depth discussions and exchanges on the latest progress and trends about smart energy systems and the Internet of Things. In the keynote speech session, a number of leading authorities in the field shared their research results and insightful perspectives, which provided participants with valuable academic insights and set the tone for the conference. In the oral presentation and poster sessions, participants gained more opportunities to demonstrate their research results and interacted directly with each other, which promoted the collision of academic ideas and deepened the cooperation and exchange. The Proceedings, a collection of accepted papers from SEEIoT 2024, covers a wide range of aspects such as Smart Grid, Intelligent Transmission and Distribution, Power Grid Dispatching and Automation Control, Power System Condition Monitoring, Energy Management for Microgrids, Ubiquitous Electric Internet of Things, Intelligent Energy Management, etc., which fully demonstrates the latest research trends and practical achievements in the fields of smart energy systems and the Internet of Things. It not only captures the essence of the conference discussions and presentations, but also serves as a valuable resource for researchers, practitioners, and policymakers alike. The successful organization of SEEIoT 2024 not only promotes academic exchanges and technical progress in the fields of smart energy systems and the Internet of Things, but also strengthens international cooperation and ties. And the international influence of the conference has been significantly enhanced, which has injected new vitality and impetus for the future development of relevant domains and industries. Finally, we extend our heartfelt gratitude to all the authors, reviewers, sponsors, organizers, and participants who made SEEIoT 2024 a truly remarkable event. Meanwhile, we also expect that the SEEIoT series of conferences will continue to play an important role in promoting international academic exchanges and technical cooperation, and contribute to the prosperity and development of smart energy systems and the Internet of Things. The Committee of SEEIoT 2024 List of Committee Member is available in this PDF.

Minimum Risk Quantification Method for Error Threshold of Wind Farm Equivalent Model Based on Bayes Discriminant Criterion

The error threshold is the cornerstone to balance the mathematical complexity and simulation speed of wind farm (WF) equivalent models, and can promote the standardization process of equivalent methodology. Due to differences in power system conditions and model evaluation standards in different countries, the form and indexes of error thresholds of WF equivalent models have not been unified yet. This paper proposes a theoretical method for quantifying the minimum risk of error threshold of WF equivalent models based on the Bayes discriminant criterion. Firstly, the Euclidean errors of WF equivalent models in different periods are quantified, and the probability density distributions of the errors are fitted by kernel density estimation. Secondly, the real-time weighted prior probability algorithm is used to obtain the prior probability of a valid WF equivalent model, and the different losses caused by the missed judgment and misjudgment of the model validity to power systems are taken into account. Thirdly, the minimum risk quantification model of error threshold is established based on the Bayes discriminant criterion, and the feasibility of the proposed method is verified by an actual WF with numerical examples. Compared with the existing error thresholds, the proposed error threshold can more quickly and accurately determine the validity of WF equivalent models.

Multi-Period Optimal Transmission Switching with Voltage Stability and Security Constraints by the Minimum Number of Actions

Due to the ever-growing load demand and the deregulation of the electricity market, power systems often run near the stability boundaries, which deteriorates system voltage stability and raises voltage issues for the stable operations of power systems. Transmission switching (TS) has been applied to improve economic benefits and security operations for many applications. In this paper, a multi-period voltage stability-constrained problem (MP-VSTS) is established, intending to improve voltage security and the stability of a power system. Considering the online application of transmission switching, the minimum number of switching actions is taken as the objective function of the proposed MP-VSTS problem, which extends the TS application for real industries. The proposed model provides the switching lines for the upcoming period and the state of power systems for several successive periods. To overcome the solving difficulties of the proposed model, a two-stage approach is presented, which balances speed and accuracy. Numerical studies on the IEEE 118- and 662-bus power systems have demonstrated the proposed approach’s performance.

High-Precision Bi-Directional Beam-Pointing Measurement Method Based on Space Solar Power Station System.

In the process of wireless energy transmission from a Space Solar Power Station (SSPS) to a satellite, the efficiency of energy transmission is closely related to the accuracy of beam control. The existing methods commonly ignore the impact of array position, structural deviation of the transmitting antenna, and modulation errors, which leads to the deviation error in actual energy transmission beams and the reduction of energy transmission efficiency. This paper innovatively proposes a high-precision bi-directional beam-pointing measurement method, which provides a technical basis for advancing the beam-pointing control accuracy from the perspective of improving the beam-pointing measurement accuracy. The method consists of (1) the interferometer goniometry method to realize high-precision guiding beam pointing measurement; and (2) the power field reconstruction method to realize offset angle measurement of the energy-transmitting beam. Simulation results demonstrate that under dynamic conditions, the guiding beam-pointing measurement accuracy of this method reaches 0.05°, which is better than the traditional 0.1° measurement accuracy based on the guiding beam. The measurement accuracy of the offset distance of the energy center is better than 0.11 m, and the measurement accuracy of the offset angle is better than 0.012°.

Comparative Analysis of the Extreme Accuracy in Calculations of Steady States of Power Systems with Modern Software Systems

Comparative Analysis of the Extreme Accuracy in Calculations of Steady States of Power Systems with Modern Software Systems

Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a Bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.

Physical model learning based false data injection attack on power system state estimation

The cyber security of power system state estimation (PSSE) is crucial, and its robustness against evolving false data injection attacks (FDIA) is being rigorously assessed to develop advanced countermeasures. Existing FDIA methods have achieved satisfactory success rates but often fail to align with practical constraints such as the assumption of partial or complete knowledge of the power system network by the attacker, modifications in generator output measurements, and the sparsity of the attacks. This work proposes a near practical, stealthy approach using a deep generative adversarial network-long short-term memory autoencoder (GAN-LSTMAE) learning based sparse FDIA method against AC PSSE, leveraging only measurement data. To evade the bad data detection (BDD) mechanism effectively, an LSTMAE-based PSSE mimic is proposed, further optimizing the GAN-based attack generator to embed the physical laws of the system along with measurement residuals and temporal dependencies of states to the generated false data. The proposed modified training data preparation algorithm, coupled with the attack sub-graph method, defines the optimal attack region while keeping generator output measurements intact. The generated attack is validated extensively using IEEE 14 and 118 bus test benchmarks against various defense techniques, demonstrating high success rates.

Developed square-root cubature Kalman filter-based solution for improving power system state estimation with unknown inputs and non-Gaussian noise

Understanding the ever-changing dynamics of power systems is crucial, and dynamic state estimation (DSE) plays a vital role in achieving this. However, traditional nonlinear Kalman filters (NKFs) face limitations: lack of access to control inputs and presence of non-Gaussian noise in measurements, impacting their accuracy and robustness. This research introduces a novel robust DSE method that tackles these challenges head-on. For the first time in DSE, it leverages the predictive power of Holt-Winters Triple Exponential Smoothing to model the time-varying behavior of control inputs. This innovative approach allows for the simultaneous estimation of dynamic state variables such as the rotor angle and rotor speed changes, as well as transient voltages and control inputs like mechanical input torque and excitation voltage, even in the presence of non-Gaussian noise. Furthermore, the method employs modified projection statistics and a Cauchy function. This unique combination effectively bounds the influence of observation outliers while maintaining high statistical estimation efficiency. This innovative approach utilizes a square cubature Kalman filter (SCKF) for enhanced numerical stability. Extensive simulations under various anomalous conditions demonstrate the method's superior accuracy and efficiency in estimating the state vector. These results highlight its potential to significantly improve power system estimation and pave the way for real-time applications.

ЩОДО ВІДНОВЛЕННЯ ПАРКУ ТУРБОГЕНЕРАТОРІВ ЕЛЕКТРОСТАНЦІЙ УКРАЇНИ

The current state of the electric power system of Ukraine is considered. The basic principles of restoration and modernization of the generating equipment of the country's power plants in the postwar period are formulated. It is proposed to restore generation at a fundamentally new level with the involvement of advanced achievements of the world and domestic electrical engineering. When restoring destroyed power units, turbine generators with improved performance characteristics should be used selectively, taking into account the nature of the load, the geographical location of the plant, the availability of electrical connections, etc. First of all, we are talking about fully air-cooled machines with enhanced maneuverability (including asynchronized ones). The main advantages of such turbogenerators are named and the elements and components of the structure are identified, the technical condition of which is generally a determining factor in ensuring reliable operation. Examples of technical solutions to improve stator reliability are given. Ref. 14, fig. 6, table.

Power system reliability assessment technique and modeling approach based on quantum computing theory

Aiming at the reliability assessment of power system, the state sampling process as a critical step requires to generate power system states one by one using classical computer. Due to the quantum superposition, all system states can be generated at once using the quantum computing and then assess the reliability, and thus it will improve the assessment efficiency. However, how to sample system states and assess reliability using the quantum computing theory will be very important. Therefore, this paper proposes a power system reliability assessment technique and its modeling approach based on the quantum computing theory. Firstly, this paper develops a quantum model of power system reliability (including reliability models of electrical device and system) and proposes a new system state sampling method based on the quantum computing theory, which samples all system states at once and improves the sampling efficiency. Secondly, this paper proposes a quantum computing theory based on reliability assessment technique and its modeling approach for the reliability indices: Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). Thirdly, this paper uses an iterative quantum amplitude estimation (IQAE) algorithm to measure the reliability indices for improving the measurement accuracy. Finally, this paper investigates both designed Case and RBTS, and results shows the correctness and effectiveness of reliability assessment of power system based on the quantum computing.

Multi-rate measurement fusion for power system state estimation

Abstract In recent years, the estimation of power systems has become increasingly critical. The introduction of Synchronized PMU has significantly enhanced the dynamic estimation capabilities of power systems. However, given the widespread deployment and usage of SCADA systems in the power grid, it is challenging to entirely replace SCADA systems with PMU systems in a short period. Therefore, utilizing a hybrid approach that combines the two systems for power state estimation is of utmost importance. This article achieves the integration of multi-rate and multi-sensor information in the power system by employing predictive interpolation on SCADA to match the sampling rate of PMU. The performance of power grid state estimation surpasses that of a single SCADA deployment.

Joint detection and state estimation based on GPS spoofing attack in smart grids

Phasor Measurement Units (PMUs) are widely used in power system state estimation due to its high sampling rate and real-time estimation of power state in power systems. However, it is also vulnerable to cyberattacks (GPS spoofing attacks) since it uses unencrypted GPS civil signals for synchronization. The GPS spoofing attacks (GSAs) will intentionally manipulate the time reference of PMUs, which is equivalent to falsifying the phase angle of the PMU measurements. In this paper, we consider the secure power state estimation and attacks detection with the unknown GSAs in the power systems. Particularly, we derived the PMU-based GSAs model in the power state estimation problem, which is formulated as a mixed maximum likelihood problem. In the formulated problem, the involved unknown parameters are coupled and the objective function is non-convex, which are of significant challenges in finding optimal solutions. To tackle these challenges, a joint maximum a posteriori and maximum likelihood (JMAP-ML) algorithm is proposed to securely estimate the power state and detect the GSAs in the mixed measurements of PMUs. Different testing scenarios in IEEE 14 bus system are simulated to show the proposed algorithm’ performance on GSAs detection and state estimation. Numerical examples demonstrate the improved accuracy of our algorithm compared with classical algorithms when GSAs are present. And we conclude that when a sufficient number of PMUs are deployed in the system, the impact of GSAs will be largely compensated in the estimation stage.

Charging Station Power System Data Prediction Model Based on Deep Learning

The increase growth and adoption of electric vehicles (EVs) are playing a crucial role in advanced transportation system, helping to minimizing the emissions of harmful greenhouse gases and enhancing environmental sustainability. The need for EVs to be charged immediately has gained significant attention due to the rise in EVs sales over the last years. As a result of this, need for electric car charging is important to reducing the impact of electric network and providing minimum charging fares. In order to calculate the demand for charging EVs, a novel Deep Learning (DL)-based Long-Short Term Memory (LSTM) recurrent neural network predictor model is attempted to be developed in this research study. The Modified Aquilla Optimizer Algorithm (MAOA) is used to optimizing the parameter of the new Deep LSTM (DLSTM) neural predictor models and Independent Component Analysis (ICA) is utilized to solve the input time series data while conserving its properties. In this research, a novel ICA—AOA—DLSTM neural predictor model was developed to addressed the challenges of vanishing and exploding gradient in basic recurrent neural learnings. The predictor model was tested on the EV charging dataset from Georgia Tech, Atlanta, USA, indicating its performance. During simulation, the predictor achieved a prediction accuracies of 96.24% with a mean absolute errors of 0.1083 and a RMSE errors of 3.0629 × 10^-5, outperforming previous techniques. Additionally, the mean absolute error was found to be 0.2083, with a mean square error of 3.25516 × 10^-10. These results highlight the effectiveness of the novel deep learning LSTM neural predictor for this dataset compared to existing techniques.

Physics-Informed Neural Network-Based VSC Back-to-Back HVDC Impedance Model and Grid Stability Estimation

With the increase in the number of power electronic devices in power systems, various techniques for assessing their stability have emerged. Among these techniques, impedance model-based stability analysis techniques have been widely used. However, conducting such analyses across multiple operating points requires abundant impedance measurement data from power electronic devices. In this paper, we propose a method for constructing impedance models of equipment with fewer impedance measurement data in voltage-source converter (VSC) back-to-back high-voltage direct current (HVDC) systems using physics-informed neural networks. Furthermore, given the power system states, we present a neural network approach to estimate grid stability at different operating points. Validation via PSCAD/EMTDC simulations and a PyTorch neural network confirmed the adequacy of these models.

Complex-Valued Projection Statistics for Hybrid Power System State Estimation

Complex-Valued Projection Statistics for Hybrid Power System State Estimation

Mitigating sub-synchronous oscillation using intelligent damping control of DFIG based on improved TD3 algorithm with knowledge fusion

The occurrence of sub-synchronous oscillation (SSO) phenomenon in doubly-fed induction generators (DFIGs)-based wind turbines threatens the secure and stable operation of the power grid. Conventional sub-synchronous damping controllers encounter challenges in adapting to the dynamic operating conditions of power systems. This paper introduces an Intelligent Sub-Synchronous Damping Controller (I-SSDC) for DFIGs that integrates deep reinforcement learning (DRL) and knowledge to address the limitations of conventional methods for SSO mitigation. The initial step involves formulating a framework for I-SSDC using the improved twin delayed deep deterministic policy gradient (TD3) algorithm incorporating Softmax. Following this, a surrogate model is constructed, employing Weighted Linear Regression and regularization. This model is designed to identify the predominant influencing factors of SSO, focusing on the selection of the output signal (installation position) to optimize decision-making in I-SSDC. The objective is to enhance the controller’s environmental adaptability and interpretability. Moreover, knowledge and experience related to SSOs are integrated into agent training to improve the exploration efficiency of the agent. Case studies under various operating conditions of the test power system validate the efficacy of the proposed I-SSDC in suppressing SSOs.