Power System Security Research Articles

Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook

With the current transitioning and increasing complexity of power systems owing to the continuous integration of distributed generators (DGs) and Flexible AC Transmission Systems (FACTS), power system quality and security studies have extended to incorporate the impacts of these technologies. This paper presents a review of the operation and reliability impacts of FACTS technologies in improving power quality and security in modern Cyber-Physical Power Systems (CPPS). While introducing DG to the power system helps to decentralize the network for easy accessibility and enhances clean energy system, it creates new challenges such as harmonics, voltage instability, and frequency distortion. These challenges can be tackled with FACTS devices which are flexible and dynamic smart electronic controllers used to stabilize power system parameters to improve power quality and reliability. This paper examines the current state-of-the-art optimization techniques and artificial intelligence and/or computational techniques for optimal placement and operation of FACTS devices. This review highlights the generational advancement of FACTS technologies and the different objectives of optimal placement and operation of these devices. Moreover, the concept of CPPS is discussed with the potential utilization of distribution-FACTS (D-FACTS) devices for network security. Furthermore, a bibliometric analysis was carried out to show research trend of FACTS utilization. The result presents future trajectories for power utility industries and researchers interested in power system optimization and the application of FACTS technologies in smart power system networks. Some of the significant findings leads to proposed demand-side management for placement of DGs and FACTS technologies as a more strategic optimal system sizing to minimize cost. It was also concluded that future design of FACTS/D-FACTS devices must consider and appreciate interactions with the automated systems of CPPS to enhance effective integration. To this end, design modification of the operational configuration of these devices with sensors for real-time synchronized control and interaction with other CPPS technologies is an area that requires more research attention in the future.

Preventive control of successive failures in extreme weather for power system resilience enhancement

Successive failure propagating through a power system, typically caused by extreme weather such as storm and lightning, can cause blackouts by means of a variety of processes. To address this issue, this paper proposes preventive control for successive failures of power system to mitigate the consequences of successive failures in extreme weather. First, based on the Poisson process theory, the authors model the successive failure propagation, and present the confidence interval estimation for the time interval between successive failures. Then, a failure pre-control methodology that quantifies the grid regulating ability and actions is developed to make decisions for system operators on mitigating overloads of transmission lines. Furthermore, a systematic pre-control framework for successive failures is first proposed to detect latent risks, eliminate predictable consequential outages, and secure power system stability. Results from IEEE-9 bus and IEEE-39 bus standard systems as well as a real-world system show the feasibility and effectiveness of the proposed method. The proposed preventive control framework can provide a systematic pre-control action scheme for system operators, and realize enhancement of stability and resilience to power system failures.

Editorial: Security and Resilience Enhancement of Future Smart Grid: Models and Methods

The modern power systems are moving towards the future smart grid where increasing penetration of 13 renewable energy sources (RES) and power electronics is expected. While enabling the realization of 14 low-carbon power systems, the high penetration of RES reduces the security margin and resilience of 15 power systems against extreme events and disturbances. Meanwhile, power systems suffer from 16 increased vulnerabilities and security risks due to the growing integration of information and 17 communication technologies (ICT) in this ongoing transformation. As the consequences of power 18 system interruptions can be disastrous, enhancing the security and resilience of power systems with 19 practical methods has become a critical topic for the power industry. We would like to thank all the participating authors for submitting their works to this research topic.We are thankful to the reviewers for their valuable efforts in the review of all the articles considered 65 for this research topic in a most efficient and efficacious manner. We are also thankful to the

Grid-Connected Power Converters: An Overview of Control Strategies for Renewable Energy

The move towards a greener energy mix to fight climate change propels investments in converter-interfaced resources such as wind and photovoltaics, energy storage systems and electric vehicles. The ongoing evolution of the power system is occurring at a very fast pace, challenging transmission and distribution system operators to seek solutions that are not only adequate for this moment but also for future scenarios. Ongoing research in the fields of power electronics, power systems and control aims at developing control strategies that will help the energy transition to occur, while keeping a stable, secure and reliable power system. The objective of this paper is to present a critical review of the control strategies developed for grid-connected power converters found in renewable energy systems, energy storage systems and electric vehicles. The impact of grid-connected converters on the stability of power grids is also reviewed, highlighting the promising control strategies for enhancing system stability.

A Novel Condenser Vacuum Degree Prediction Model Based on LSTM and MemN2N

Condenser vacuum degree prediction of power plants is a challenge task in power system security field. Most existing studies are based on shallow machine learning algorithms, which fail to leverage historical data comprehensively, resulting inaccuracy and unreliable predictions. Therefore, using a serialization model like Recurrent Neural Network to capture time-series information from historical data is necessary. However, these serialization model alone has inherent defects in dealing with long-distance dependence, which may cause historical information forgetting problem. This paper proposes a new prediction model combining LSTM and End-To-End Memory Network (MemN2N). We use LSTM to mine the long-distance dependency information in historical data, and introduce the encoding historical information into the memory pool of MemN2N. MemN2N allows better preservation of historical information for serialization model LSTM, and can make accurate and reliable predictions through soft attention mechanism. Through the experiments on real data from the power plant show that, compared with other prediction models, the model proposed in this paper achieves higher prediction accuracy and has great engineering value.

Improvement of Power Quality in Wind Energy Conversion Systems

Abstract: Wind Energy Conversion Systems (WECS) show variability in their output power as a result of changing their main engines (wind speed). This introduces a new grid uncertainty factor and poses many challenges to electricity system designers and utilities in terms of grid network integrity, ie power system security, power system stability and power quality. This paper discusses the various challenges of wind energy when integrated into the grid and identifies different mitigation strategies for its smooth integration. Keywords: wind energy system, Power quality, Power filters, Reactive Power, controllers

Real-time and consistent sparse estimation of power system distribution factors using online adaptive elastic-net

Distribution factors (DFs) are vital for power system monitoring and secure operation. However, it is challenging to accurately estimate dominant DFs while promoting the sparsity of DFs in real time, especially in large-scale power systems with high-dimensional data. This paper proposes a novel Online Adaptive Elastic-net (AdaEnet) method to address this challenge, which allows the real-time and consistent sparse estimation of DFs in large-scale power systems. The problem is formulated as a sparse regression problem using data measured by phasor measurement units. The AdaEnet estimator is advocated to promote the sparsity of DFs, and a LARS-AdaEnet algorithm is developed for the real-time estimation of DFs. Thanks to the oracle properties and the grouping effect of the AdaEnet, the method yields unbiased dominant estimates with stability to collinear predictor variables, even for underdetermined systems. Moreover, due to the online updated adaptive weights and the early-stopping property of the LARS-AdaEnet, the method efficiently solves the estimator using only a few latest samples. Test results on the IEEE 300- and the Polish 3375-bus systems demonstrate that the proposed method achieves high estimation consistency and real-time adaptability in the presence of high-dimensional data (i.e., massive collinear predictor variables of small sample size). Hence this method promotes the data-driven, sparsity-aware, and computationally efficient sensitivity analysis of active power line flows in large-scale power systems. It can broadly facilitate real-time situational awareness and decision-making for improving power system security.

Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest

Climate change, global warming, pollution, and energy crisis are the major growing concerns of this era, which have initiated the electrification of transport. The electrification of roadway transport has the potential to drastically reduce pollution and the growing demand for energy and to increase the load demand of the power grid, thereby giving a rise to technological and commercial challenges. Thus, charging load prediction is a crucial and demanding issue for maintaining the security and stability of power systems. During recent years, random forest has gained a lot of popularity as a powerful machine learning technique for classification as well as regression analysis. This work develops a random forest (RF)-based approach for predicting charging demand. The proposed method is validated for the prediction of public e-bus charging demand in the city of Helsinki, Finland. The simulation results demonstrate the effectiveness of our scheme.

Design of power system excitation auxiliary controller based on Multi-Agent

The continuous access of new energy to the power grid increases the complexity of the structure of the power system, which brings hidden dangers to the security of new energy power system. In order to further enhance the operation stability and security of new energy grid, a generator excitation auxiliary controller based on multi-agent is proposed in this paper. Firstly, the multi-agent model of power system is established, and then the distributed output feedback controller is designed for the generator model. The parameters of the controller are calculated and solved by H ∞ theory. Finally, the effectiveness of the controller designed is verified by simulation analysis.

Deep Learning based Load Forecasting with Decomposition and Feature Selection Techniques

The forecasting of short term electricity load plays a vital role in power system. It is essential for the power system's reliable, secure, and cost-effective functioning. This paper contributes significantly for enhancing the accuracy of short term electricity load forecasting. It presents a hybrid forecasting model called Gated Recurrent Unit with Ensemble Empirical Mode Decomposition and Boruta feature selection (EBGRU). It is a hybrid model that addresses the non-stationary, non-linearity and noisy issues of the time series input by using Ensemble Empirical Mode Decomposition (EEMD). It also addresses overfitting and curse of dimensionality issues of load forecasting by identifying the pertinent features using Boruta wrapper feature selection. It effectively handles the uncertainty and temporal dependency characteristics of load and forecasts the future load using deep learning based Gated Recurrent Unit (GRU). The proposed EBGRU model is experimented by using European and Australian Electricity load datasets. The temperature has high correlation with load demand. In this study, both load and temperature features are considered for the accurate short term load forecasting. The experimental outcome demonstrates that the proposed EBGRU model outperforms other deep learning models such as RNN, LSTM, GRU, RNN with EEMD and Boruta (EBRNN) and LSTM with EEMD and Boruta (EBLSTM).

Analysis of Fast Frequency Response Allocations in Power Systems With High System Non-Synchronous Penetrations

An inevitable consequence of the power system transition towards nearly 100% renewable-based generation is the loss of synchronous generators with their associated inertia, frequency, and voltage control mechanisms. Battery energy storage with different converter technologies, due to their fast-ramping capabilities, are expected to address these challenges and replicate functionalities that so far have been provided by conventional generators. This research presents an in-depth dynamic analysis for the impact of grid forming and grid following battery energy storage locations on the frequency metrics. Performance comparisons that account for the interactions between storage technologies and the decrease of regional inertia are also investigated via dynamic simulations for the projected 90% inverter-based generations in Ireland in 2030. The analysis is conducted using a high-fidelity model of the 39 Bus system, which was modified in DIgSILENT, to best replicate a real frequency event that occurred in the Irish power system. Finally, the empirical findings provide a new understanding of the optimal placement of grid-scale fast storage technologies to maintain power system security at high renewables.

Large-Scale Preventive Security-Constrained Unit Commitment Considering N-k Line Outages and Transmission Losses

This paper presents a new formulation for the preventive security-constrained unit commitment problem modeling <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> line outages and transmission losses. The pre- and post-contingency transmission constraints, representing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> line outages, are explicitly included by using generalized generation distribution factors. To account for security, a contingency selection procedure based on line outage distribution factors finds a list of worst-case contingencies. Transmission losses are incorporated using piecewise linear expressions. The proposed model is formulated as an instance of mixed-integer linear programming. The effectiveness of the proposed approach is illustrated with the IEEE 57-bus system and the 1,354-bus portion of the European transmission system. As empirically evidenced, the explicit consideration of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N-k</i> line outages and transmission losses leads to different decisions in the generation scheduling and dispatch, ensuring secure power system operation.

Power system security enhancement in FACTS devices based on Yin–Yang pair optimization algorithm

Power system security enhancement in FACTS devices based on Yin–Yang pair optimization algorithm

A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems

The energy industry, primarily based on the use of fossil fuels (e.g., coal and oil) is rapidly shifting toward renewable energy for securing sustainable resources. Thus, preparing for large wind power ramp events is essential to retain reliable and secure power systems. This study proposed a new statistical approach to predict wind power ramp events, and evaluated the performance of prediction. The empirical data, which is the observed wind power output data and wind speed data from Taebaek (South Korea) were used for analyzing ramp events and for evaluation. Based on the data analysis, a practical metric for evaluating the performance of wind power ramp events forecasting was developed and presented in detail. Notably, the accuracy of forecasting was evaluated through various metrics, whereas the normalized mean absolute error (NMAE) analysis demonstrated ≤ 10% values for all the analyzed months. In addition, a system review was conducted to check if the methodology suggested in this study has helped enhance the security of power systems. The results show that evaluating and considering the ramp events can improve the accuracy of wind power output forecasting which can secure the smart energy systems.

A binary particle swarm optimization approach for power system security enhancement

Improvement of power system security manages the errand of making healing move against conceivable system overloads in the framework following the events of contingencies. Generation re-dispatching is answer for the evacuation of line overloads. The issue is the minimization of different goals viz. minimization of fuel cost, minimization of line loadings and minimization of overall severity index. Binary particle swarm optimization (BPSO) method was utilized to take care of optimal power flow issue with different targets under system contingencies. The inspiration to introduce BPSO gets from the way that, in rivalry with other meta-heuristics, BPSO has demonstrated to be a champ by and large, putting a technique as a genuine alternative when one needs to take care of a complex optimization problem. The positioning is assessed utilizing fuzzy logic. Simulation Results on IEEE-14 and IEEE-30 bus systems are presented with different objectives.

Control of Distributed Photovoltaic Inverters for Frequency Support and System Recovery

Replacing conventional synchronous generator-based power plants with inverter-based renewable energy resources results in a reduction of the inertia in power systems. To sustain the security and reliability of these low-inertia power systems, frequency support is increasingly required in new standards for grid-connected renewable energy resources, especially distributed photovoltaic (DPV) systems. This article proposes a frequency droop-based control in DPV inverters to improve frequency response in power grids with high penetration of renewable energy resources. A predefined power reserve is kept in the DPV inverter, using flexible power point tracking. The proposed algorithm uses this available power reserve to support the grid frequency. Furthermore, a recovery process is proposed to continue injecting the maximum power after the disturbance, until frequency steady-state conditions are met. The positive inertia using the proposed recovery mode, leads to recovery of the frequency in a shorter period. A composite load model of a distribution feeder, including DPV, is developed to assess the effectiveness of the proposed frequency support algorithm in power systems with high penetration of DPV inverters. The dynamic performance of the proposed frequency support algorithm with over frequency and under frequency events is also evaluated experimentally.

EXPERIMENTAL RESEARCH OF LINE POWER UNLOADING AUTOMATICS WITH ITS TRIPPING CHARACTERISTICS CORRECTION

In conditions of complicated structure of modern power grids, a special emphasis is given to the issues of ECA failure-free operation. This article is concerned with the problems of increasing sensitivity and selectivity of line power unloading automatics because its correct operation plays an important role in ensuring energy security and survivability of power systems.&#x0D; &#x0D; Within the framework of this paper, the full cycle of development of ECA algorithm is considered. ECA is performing the functions of line power unloading automatics. The development cycle includes the tasks of tentative assessment of electrical equipment operating modes, rationale for choosing the automatics tripping characteristics, algorithm design and programmable logic controller (PLC) based implementations. It also includes the laboratory testing with the use of real-time hardware and software complex.&#x0D; &#x0D; The calculation data of steady-state and transient operating modes of power electrical equipment presented in the article were carried out in software complexes RastrWin, Rustab. The power facilities of the interconnected power systems of Urals, Siberia and Kazakhstan were considered as modeled objects of power systems. In accordance with the obtained results, the tripping characteristics of device being developed were determined.&#x0D; &#x0D; Based on the results of mathematical simulation of electrical equipment transient operating modes, the major disadvantages of device algorithm, which controls the active-power flow through the line, were determined. Besides, methods focused on increasing the sensitivity and selectivity of such automatics are proposed.&#x0D; &#x0D; The developed algorithm controls the active-power flow over a given cross section. Measurements of electrical parameters are carried out at one of the lines in cross section. The total value of active-power flow over the cross section is calculated according to the equivalent circuit. To increase the sensitivity and selectivity of automatics, the algorithm uses the action time adjustment. It is determined depending on the previous operating mode of transmission line.&#x0D; &#x0D; The algorithm for line power unloading automatics described in this article was implemented on a PLC system in accordance with the IEC 61131 standard. The obtained device was successfully tested on the real-time hardware and software complex. This paper presents the results of the carried out device performance tests, and the characteristics of the used mathematical simulation.

Early warning method for power supply service quality based on three-way decision theory and LSTM neural network

An efficient early-warning method for power supply service quality(PSSQ) is of great significance to optimize the customer experience of power service and ensure the security of power systems. Based on the power service data from the customer side, this paper proposes a early-warning method for PSSQ, which is based on three-way decision theory and long–short term memory (LSTM) network. First of all, four early-warning indicators (i.e., the proportion of complaints work orders indicator, the proportion of responsible work orders indicator, the proportion of duplicate work orders indicator and the average processing time indicator) of PSSQ are proposed according to requirements from the customer side. Secondly, the early-warning value of PSSQ is determined based on LSTM neural network and historical data. Then, the threshold of early warning decisions is determined based on the three-way decision theory. Finally, three local power supply companies in Zhejiang province are taken for case study to prove the effectiveness of the PSSQ early-warning method proposed.

A novel hybrid model for short-term prediction of wind speed

Due to the randomness and contingency of wind speed size and direction, it is difficult to predict the wind speed accurately, which seriously affects the stable operation of the power system. To improve the operation stability of power system, the accurate prediction of wind speed is very important. In this paper, a new hybrid model based on gray wolf algorithm (GWO) and support vector machine (SVM) for wind speed prediction is proposed. Firstly, Neo4j(NE) is utilized to identify the data and preprocess the data. Secondly, k-means clustering(KC) is utilized to analyze data and eliminate invalid data. Thirdly, GWO is utilized to optimize the kernel function parameters and penalty factors of SVM to improve the prediction results. Fourthly, The four modules are combined into NE-KC-GWO-SVM model to predict the wind speed accurately. Finally, to verify the effectiveness of the proposed model, the prediction accuracy of the model is experimentally analyzed from two parts. One is to analyze the superiority of the model itself by using the method of single model removed. The results show that the proposed model is the best, and has high accuracy, and can reflect the characteristics of wind speed well and truly. The other one is that models similar to those proposed in the literature are selected for comparative analysis. The experimental results show that compared with the other two models, the proposed model has the best accuracy. At the same time, the proposed model has good prediction stability and acceptable time complexity. Based on all the experimental results, it can be obtained that the proposed model has better prediction effect, which can provide a scientific basis for the macro-control of power system and improve the operation security and stability of power system.

Preventive Scheduling for Reducing the Impact of Glaze Icing on Transmission Lines

The icing on transmission lines threatens the power system security while the heat caused by power transmission losses can prevent icing growth. This paper proposes a preventive scheduling model for mitigating the glaze icing by optimizing the distribution of power losses on ice-coated transmission lines. An analytical glaze icing growth model is established based on the heat balance theory, which builds a direct relationship between icing growth and power transmission losses. Accordingly, the analytical glaze icing model is embedded into the proposed scheduling model to quantify the impact of power system schedules on transmission line icing and reduce the glaze icing of transmission lines. The scheduling model co-optimizes active power dispatch, demand response, and reactive power optimization for promoting the de-icing effect. To overcome the computational difficulties, the analytical glaze icing growth model is further linearized, and the Lagrangian relaxation method is adopted for identifying a practical solution. Case studies are conducted on different icing scenarios in the IEEE RTS-79 test system to verify the validity of the proposed model. Results show that the proposed preventing scheduling model can avoid the icing on transmission lines for mild ice disasters, while efficiently restraining the icing growth on transmission lines when they cannot be completely de-iced in severe storms.