Large Power Systems Research Articles

Optimal design and operation of damping controllers in PV–wind integrated sustainable energy grids considering system uncertainties

AbstractThis paper aims to present an optimization method for the best bus selection (BBS) in the large‐scale power systems in order to send the input signals to the damping controllers. In this regard, a submodular function is first defined to calculate the distance between the unstable vectors of the system and the controllability Gramian leading to find a set of buses for installation of the damping controllers. Based on the results obtained from the BBS approach, the number of candidate buses will be increased to deal with the inter‐area oscillations by introducing an event‐triggered based auxiliary scheme. According to the candidate buses and the power system operating condition, the most critical buses of the candidate set is allowed to participate as the input signals of damping controllers to reduce the inter‐area oscillations. The proposed algorithm is implemented on an interconnected large power system in the presence of wind and solar farms. These renewable energy resources are connected to the power system through the multi‐terminal DC network, so that their power products can be damped and injected into the power grid by designing the damping controllers. The simulations have been analysed using MATLAB software under different scenarios.

A Power Flow Calculation Method Considering High-order Load Models

Given the popularity of new energy and the connection between large power electronic equipment and power system, the operation of power system becomes more complex and requires higher accuracy of power flow calculation. Therefore, a power flow calculation method considering high-order load model is proposed. On the basis of the Newton-Raphson method, when considering the load voltage characteristics, it is necessary to change the variables in the Newton-Raphson method power flow. Therefore, the established polynomial load model is introduced into the Newton-Raphson method, and the effect becomes more obvious with the increase of the order. Finally, constant power, constant current, constant impedance load and fifth order load models are tested in IEEE14-bus system respectively to verify the good accuracy and robustness of the proposed method.

Dynamic Simulation of Three Phase Induction Machines Based on Reduced Order Model for Power Systems Analysis

Induction machines are a crucial type of electrical equipment utilized as motors in various industries and single-phase forms in household applications. They make up more than 80% of the industrial motors used today. This paper presents a dynamic simulation of three-phase induction machines based on the (d-q) model, providing a clear and easy-to-understand explanation of the behavior of the induction motor in the synchronous reference frame. The simulation is implemented using SIMULINK/MATLAB software, and full-order model analysis is conducted for transient analysis. However, modeling induction machines as part of power system analysis can be done with a less detailed model than the full-order one. In this paper, a reduced-order model is employed to simulate the induction motor using MATLAB/SIMULINK. The results of the reduced-order system and the full-order framework are compared to investigate the model's limits, and the computational benefits of saving time during large power system simulations justify the relative decrease in accuracy. The use of a reduced-order model results in a significant reduction in computation time when simulating large and very large-scale power systems, with a much higher accuracy in transient analysis than the conventional steady-state model.

Towards Progressively Detecting Faults in a Large Power System: A Visual Analytics Approach

Electricity serves as the foundation of everyday life. Grid operators are obliged to provide stable power supply to end users. However, given the scale and complexity of modern nationwide grids, it has become increasingly challenging to locate faults quickly and accurately during power failure. Previous locating techniques mainly focus on data analyzing methods, while little work is done using visual analytics approach. This paper presents a progressive visual analytics procedure (i.e., overview, detecting faulted bus candidates, locating buses, and looking into bus details) to detect fault location and figure out possible faulted buses. In particular, an efficient clustering algorithm associated with a novel visualization for time-varying series as well as multiple and coordinated views is developed to locate faulted buses intuitively and quickly. Case studies and expert feedback have indicated that our approach is contributing to real-world applications.

Damping of Inter-Area Oscillations Using TCPS Based Delay Compensated Robust WADC

Low-frequency oscillations (LFOs) pose a significant obstacle in achieving optimal power flow and utilizing transmission corridors in large interconnected power systems. In this study, linear matrix inequality (LMI) base robust wide-area damping controller (WADC) is introduced to effectively address critical LFOs. The proposed WADC utilizes wide-area signals, which modulate the phase angle of a thyristor-controlled phase shifter device. To ensure that the concerned modes of LFOs lie in left half of the complex plane, a D-space sub-region approach is applied. However, using wide-area signals introduces time delays, which negatively impact the efficacy of the WADC; hence, a time delay compensator (TDC) is incorporated into the feedback path of the signals. The proposed approach involves four key steps: selecting feedback signals using a combined modal transformation and geometric approach, designing an appropriate TDC, building a generalized plant with mixed sensitivity weights, and solving the LMI. The dynamic efficacy is verified on IEEE 4-machine 11-bus system, and 10-machine 39-bus system. Poorly damped single and multiple critical modes are placed in the D-space region of interest with specified damping ratios viz. 10% and 15%. Furthermore, damping ratios of critical modes are maintained with TDC based proposed WADC while deteriorating with other counterparts.

Proximal policy optimization through a deep reinforcement learning framework for remedial action schemes of VSC-HVDC

A proximal policy optimization (PPO)-based back-to-back VSC-HVDC emergency control strategy based on multi-agent deep reinforcement learning (DRL) approach is proposed for use in an energy management system (EMS). In this scheme, an advanced DRL algorithm is proposed by implementing both PPO and a communication neural network for large power systems. The PPO modeled as intelligent agents with objective functions have shown a higher convergence performance than have existing DRL algorithms. Further, the model was demonstrated to effectively address voltage variances caused by the high penetration of renewable energy sources. By implementing PPO, the learning procedure is stabilized and made robust to continuous changes in network topology. To escalate the effectiveness of the proposed algorithm, a comprehensive case studies were conducted on an standard test systems and Korean power system considering variations in load and PV generation and a weak centralized communication environment. The results indicate that outstanding control performance and autonomously regulated bus voltage and line flows, thereby validating the effectiveness of the method.

Distinguishing Between Cyber Attacks and Faults in Power Electronic Systems—A Noninvasive Approach

With the increased cyberinfrastructure in large power systems with inverter-based resources (IBRs), it remains highly susceptible to cyber-attacks. Reliable and secure operations of such a system under a large signal disturbance necessitate an anomaly diagnosis scheme, which is substantial for either selective operation of relays (during grid faults) or cybersecurity (during cyber-attacks). This becomes a challenge for power electronic systems, as their characteristic response to such large-signal disturbances is very fast. Hence, we accumulate our efforts in this article to characterize them accurately within a short time frame. A novel noninvasive anomaly diagnosis mechanism for IBRs is presented, which only requires locally measured voltage and frequency as inputs. Mapping these inputs in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$XY$ </tex-math></inline-formula> -plane, the characterization process is able to classify between the anomalies within 5 ms. To the best of our knowledge, this mechanism provides the fastest decision in comparison to the existing techniques, which also assists the equipped protection/cybersecurity technology to take corresponding decisions without enforcing any customization. The proposed scheme is validated on many systems using real-time (RT) simulations in OPAL-RT environment with HYPERSIM software and also on a hardware prototype. The results verify the effectiveness, scalability, and accuracy of the proposed mechanism under different scenarios.

A DC Estimation Method to Improve the Accuracy of DC Power Flow

The DC power flow(DCPF) method greatly simplifies the process of power flow calculation compared with the AC power flow(ACPF) method. However, when the DCPF method is applied in the larger power system, its calculation deviation will be larger due to a mass of total active power loss of the system. It will be difficult to meet the requirements of the actual applications in the grid. By using ACPF as the measured values, a DC state estimation method is proposed here to correct the parameter of the DCPF model. The case study in the actual grid proves that this method can significantly relieve the calculating deviation of the DCPF method. Meanwhile, the method can effectively improve the calculation speed and better meet the accuracy requirements of practical grid applications.

Phase shifting transformer to reduce power congestions and to redistribute power in interconnected systems

&lt;span lang="EN-US"&gt;The increased penetration of wind and solar power, as well as the liberalized electricity market, makes the power system network interconnected and complex. As the power demand is increasing daily, the complexity of operating large power systems is also increasing. Congestion in the transmission network may become more common than previously, making power flow management a problem that becomes increasingly important. Unexpected power flows (also known as loop flows) are becoming a bigger issue in today's linked power networks. These flows have a detrimental impact on the safe functioning of integrated power networks, which hinders their ability to conduct cross-border trade. Phase shifting transformers (PSTs) allow real power flow to be controlled by changing the phase shift across the device. This study deals with two interconnected parallel power system networks and the power flow controlled through a PST in between. The simulation results emphasize the importance of the PST in facilitating the transfer of energy throughout the regional transmission interconnection.&lt;/span&gt;

Simulating a Strong Magnetic Storm Impact on Russia’s Interconnected Power System of Center

Studying of a negative impact of the space weather and magnetic storms as its special case, on the performance of a critical technological infrastructure, with development of methods and means for protecting it is a relevant but difficult task. Many advanced countries, primarily the United States, are dealing with this problem. The article presents the results of elaborating a methodology for computational simulation of the impact of geomagnetically induced currents on large electric power systems. The developed methodology makes it possible to take into account the influence of changes in the reactive power and temperature of power transformer structural elements caused by magnetic biasing of their core, as well as the operation of relay protection. The impact of the geomagnetically induced currents expected during a strong magnetic storm on Russia’s Interconnected Power System of Center, including the power systems of Moscow, Leningrad and other major administrative and industrial regions of the country has been simulated for the first time. It is shown that such impact gives rise to a set of factors that can cause the development of a system-wide accident: a significant decrease in voltage at a number of power system facilities, mass-scale disconnection of power lines by their relay protections, and unacceptable heating of structural elements of some power transformers.

Variations in household water affordability and water insecurity: An intersectional perspective from 18 low- and middle-income countries.

Compounding systems of marginalization differentiate and shape water-related risks. Yet, quantitative water security scholarship rarely assesses such risks through intersectionality, a paradigm that conceptualizes and examines racial, gendered, class, and other oppressions as interdependent. Using an intersectionality approach, we analyze the relationships between household head gender and self-reported socio-economic status, and water affordability (proportion of monthly income spent on water) and water insecurity (a composite measure of 11 self-reported experiences) for over 4000 households across 18 low- and middle-income countries in Central and South America, Africa, and Asia. Interaction terms and composite categorical variables were included in regression models, adjusting for putative confounders. Among households with a high socio-economic status, the proportion of monthly income spent on water differed by household head gender. In contrast, greater household water insecurity was associated with lower socio-economic status and did not meaningfully vary by the gender of the household head. We contextualize and interpret these experiences through larger systems of power and privilege. Overall, our results provide evidence of broad intersectional patterns from diverse sites, while indicating that their nature and magnitude depend on local contexts. Through a critical reflection on the study's value and limitations, including the operationalization of social contexts across different sites, we propose methodological approaches to advance multi-sited and quantitative intersectional research on water affordability and water insecurity. These approaches include developing scale-appropriate models, analyzing complementarities and differences between site-specific and multi-sited data, collecting data on gendered power relations, and measuring the impacts of household water insecurity.

Multimachine Stability Enhancement using Fuzzy- Logic based PSS tuning with Shunt FACTS Device

In Large power system network the dynamic behaviour of system is nonlinear in nature. Due to disturbance the system stability cannot be maintained, which leads to outage of power equipment. In order to restore the system parameter after perturbance, coordination control damping is essential. Coordination control damping can be achieved by using fuzzy based PSS and STATCOM in multimachine system for sever disturbance. Due to perturbance the system loss its synchronism and the system parameter deviate from the nominal value. With the effective damping control technique proposed in this article is to minimize the integral square error of speed deviation. Two area 4-Machine 11-bus test system considered and the simulation of proposed system is developed in Matlab/Simulink R2018a.

A Robust Stackelberg Game for Cyber-Security Investment in Networked Control Systems

We present a resource-planning game for cyber-security of networked control systems (NCSs). The NCS is assumed to be operating in closed loop using a linear state feedback <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> -controller. A zero-sum, two-player Stackelberg game (SG) is developed between an attacker and a defender for this NCS. The attacker aims to disable communication of selected nodes and thereby render the feedback gain matrix to be sparse, leading to degradation of closed-loop performance, while the defender aims to prevent this loss by investing in the protection of targeted nodes. Both the players trade their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> -performance objectives for the costs of their actions. The standard backward induction (BI) method is modified to determine a cost-based Stackelberg equilibrium (CBSE) that saves the players’ costs without degrading the control performance. We analyze the dependence of a CBSE on the relative budgets of the players and on the node “importance” order. Moreover, a robust defense (RD) method is developed for the realistic case when the defender is not informed about the attacker’s resources. The proposed algorithms are validated using examples from wide-area control of electric power systems. It is demonstrated that reliable and RD is feasible unless the defender’s resources are severely limited relative to the attacker’s resources. We also show that the proposed methods are robust to time-varying model uncertainties and thus are suitable for long-term security investment in realistic NCSs. Finally, we use computationally efficient genetic algorithms (GAs) to compute the optimal strategies of the attacker and the defender in realistic large power systems.

An Improved Slow Coherent Groups Recognition Method for Dynamic Equivalence of Large Power System

The slow coherent groups recognition method is one of the dynamic equivalence techniques, which is based on the singular perturbation principle. Its algorithm performance is widely recognized. In order to reduce the solving difficulty and time consuming, this method ignores the generator stator damping effect. But during a fault, the generator stator damping effect directly affects the oscillation amplitude and frequency of the rotor angular curve. If the damping characteristics of the original system are ignored, when the damping torque coefficients of each generator vary greatly, the coherence between generators will be affected, and the accuracy of the simplified equivalent system will inevitably decrease. This paper proposes a new method based on the traditional slow coherent groups’ recognition method. The new method, which is based on the second-order model of the generator, can consider the generator stator damping effect without increasing the algorithm complexity. The active and reactive power of the system are decoupled. The eigenvalues of the system model and the general solution of the second-order differential equations are obtained through rigorous derivation. It also proposes a two-scale coherent groups recognition criterion. Finally, the effectiveness of the new method is verified based on the IEEE-39-Bus system.

Review of Discrete Fourier Transform During Dynamic Phasor Estimation and the Design of Synchrophasor Units

In a large and complex interconnected power system, the measurement of synchronized bus voltage and line current plays a vital role in the monitoring and precise control of various sophisticated electrical equipment for secure and reliable operation. Phasor measurement units (PMUs) are incorporated into a wide area of the power system to extract the different signals of synchronized phasors. In this paper, the capacity of the PMU phasor estimation algorithm is explored based on discrete Fourier transform (DFT) under different sampling frequency rates during various dynamic scenarios in accordance with the IEEE C37.118.1a-2014 standard. Furthermore, the performance of the DFT algorithm varies according to the phase angle and dynamic parameters such as frequency, frequency ramp rates, modulation frequency, harmonic levels, step change, decaying dc, and noise levels. The simulation results reveal that accuracy of the phasor estimation algorithm based on DFT can be achieved at high sampling frequency rates. Furthermore, the results of DFT-based phasor estimation are compared with Shank’s estimation method (SEM) and the least-squares estimation method (LEM). The presented method is best suited to PMU algorithms development based on DFT for better visualization of the smart electric grid.

Robust reconfiguration strategies for maximum output power from large-scale photovoltaic arrays under partial shading conditions

Extraction of maximum power from large scale solar photovoltaic power systems is the most challenging and demanding research in the current scenario. Solar photovoltaic panels are highly susceptible to a phenomenon known as partial shading. Partial shading increases mismatch losses and reduces the output of the solar photovoltaic system The output reduction in the partially shaded array is proportional to the shaded area, shaded panel’s placement within the array, panel connections, shade geometry, etc. There are several approaches for reducing Partial shading effects in the literature. The most efficient approach to mitigating the mismatch losses due to Partial shading in large-scale solar photovoltaic systems is the reconfiguration technique, which distributes shaded panels more evenly and increases the maximum power output. The current work utilizes a set of reconfiguration rules for selecting the location of shaded panels within an array that allows for multiple reconfiguration options. The results show that the proposed reconfiguration has obtained an improved Performance enhancement ratio of 25% in one shading pattern i.e. short wide shading, Performance enhancement ratio of 6.4% in short narrow and centre shading and Performance enhancement ratio of 5.9% in long narrow shading. The proposed reconfiguration was found to be the most suitable, simple, and cost-effective solution for large size of solar photovoltaic system under all shading conditions.

Electrical Event Detection and Monitoring Data Storage from Wide Area Measurement System

Synchronized phasor measurement systems are being widely used around the world and have become essential elements in the evolution of the operation of large electrical power systems (EPS). These systems, called Phasor Measurement Units (PMUs), are capable of recording and communicating dynamic data from the EPSs in a synchronized way by GPS and with a high sampling rate, generate a huge set of data that, among many applications, has the capacity to detect events. In this way, this work presents a data management system architecture applied to a real PMU system located in the state of Paraná, Brazil that detects and storages events using principal component analysis and Pearson correlation. This method can detect and store electrical events that occurred during the operation of the national interconnected system of Brazil with good results.

Multi-objective Optimization of Relay Protection Setting Based on MOAHA Algorithm

With the expansion of the power grid scale, more and more relay protection devices exist in the power system, and the optimization of node protection setting values has also begun to receive attention. Conventional genetic algorithm is mainly used in the current multi-objective optimization solution of relay protection fixed value, and its multi-objective optimization procedure is relatively complicated, resulting in low optimization solution efficiency. Therefore, applying the multi-objective artificial hummingbird algorithm (MOAHA) to the optimization process is proposed to obtain a new fixed-value multi-objective optimization method for relay protection. According to the set value requirements of the relay protection, a multi-objective optimization model of the set value of the relay protection is established, and the constraints are determined. Regarding the grid vulnerability coefficient, calculate the weighting factor corresponding to each constraint condition. Then, the multi-objective artificial hummingbird algorithm is applied to design a simple multi-objective optimization mode, including two stages of self-search and guided search to obtain fixed-value optimization results. The results of the example analysis show that the proposed optimization method only needs 20 iterations to output the optimal solution, and the average time spent to obtain the optimal solution is 17.6s. It can play a prominent role in the global optimization and tuning of relay protection in large power systems.

AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy.

Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid

Modern societies need a constant and stable electrical supply. After relying primarily on formal mathematical modeling from operations research, control theory, and numerical analysis, power systems analysis has changed its attention toward AI prediction/forecasting tools. AI techniques have helped fix power system issues in generation, transmission, distribution, scheduling and forecasting, etc. These strategies may assist today’s large power systems which have added more interconnections to meet growing load demands. They make it simple for them to do difficult duties. Identification of problems and problem management have always necessitated the use of labor. These operations are made more sophisticated and data-intensive due to the variety and growth of the networks involved. In light of all of this, the automation of network administration is absolutely necessary. AI has the potential to improve the problem-solving and deductive reasoning approaches used in fault management. This study implements a variety of artificial intelligence and deep learning approaches in order to foresee and predict the corrective measures that will be conducted in response to faults that occur inside the power distribution network of the Grid station in Tabuk city with regard to users. The Tabuk grid station is the source of the data that was gathered for this purpose; it includes a list of defects categorization, actions and remedies that were implemented to overcome these faults, as well as the number of regular and VIP users from 2017 to 2022. Deep learning, the most advanced method of learning used by artificial intelligence, is continuing to make significant strides in a variety of domain areas, including prediction. This study found that the main predictors of remedial measures against the fault occurring in the power systems are the number of customers affected and the actual cause of the fault. Consequently, the deep learning regression model, i.e., Gated Recurrent Unit (GRU), achieved the best performance among the three, which yielded an accuracy of 92.13%, mean absolute error (MAE) loss of 0.37%, and root mean square error (RMSE) loss of 0.39% while the simple RNN model’s performance is not up to the mark with an accuracy of 89.21%, mean absolute error (MAE) loss of 0.45% and root mean square error (RMSE) loss of 0.34%. Significance of the research is to provide the maximum benefit to the customers and the company by using different AI techniques.