Brazilian Interconnected Power System Research Articles

Mitigation of the commutation failure problem in the HVDC multi-infeed scenario in Brazil using synchronized phasor measurement

This paper presents the Commutation Failure (CF) problem in the High Voltage Direct Current - Line-Commutated Converter (HVDC-LCC) scenario in Brazil through the monitoring of Short-Circuit Ratio (SCR), CF indexes, Direct Current (DC) link power, and the use of simulated synchrophasor data. The analysis was performed by training a neural network using data from the year 2020, and its robustness was verified using data from the year 2021 of the Brazilian Interconnected Power System (BIPS). Additionally, an analysis of transient instability in the North-South Transmission Corridor (NSC) will be presented right after the occurrence of CF by monitoring the Lyapunov transient instability margin, with the primary objective of bringing subsidies to the control center operation area according to the severity of the CF occurrence, preventing its propagation and mitigating it through real-time predictive actions, such as power redispatch on DC links and, thus, minimizing the occurrence of CF.

The role of load and distributed energy resources modeling in voltage recovery studies

Load modeling in power systems is crucial to stability studies. The analysis of certain dynamic phenomena requires proper load modeling. Fault Induced Delayed Voltage Recovery (FIDVR) relates directly to Residential Air Conditioner (RAC) stalling. Therefore, the modeling of induction motor (IM) loads is essential to this analysis. Delayed voltage recovery in the system can result in delayed power recovery in inverters connected to the transmission system, which connects photovoltaic and wind power plants to the Brazilian Interconnected Power System (BIPS). In the last few years, there has been a considerable growth of Distributed Energy Resources (DER) in Brazil. Modern and legacy ride-through settings coexist in power systems around the world. Inverters with legacy settings are more likely to disconnect in systemic events. This creates a context that has been assessed in this paper. The modeling of induction motors in the Acre-Rondonia system leads to a deterioration of this system’s dynamic performance, which can lead to collapse. DER’s voltage support can contribute to post-fault system voltage recovery, mitigating the delay caused by RAC stalling and avoiding collapse. Thus, more robust ride-through settings are required to avoid inverter disconnection. Moreover, dynamic voltage support and reactive current priority requirements are demands from inverters connected to the system. The results obtained show that this consideration enable more consistent results than the ones obtained by the classic ZIP load model.

Physics-Guided Neural Network for Load Margin Assessment of Power Systems

The power system load margin is an important index used in power system operation centers. Traditional load margin calculation involves solving a set of differential-algebraic equations where not all information is always available. Data-driven methods such as Neural Networks (NNs) have been proposed but present poor performance and lack of generalization, especially in cases other than the operating history of power systems. In this article, we propose a Physics-Guided Neural Network (PGNN) to calculate the load margin of power systems. The PGNN is regularized by an auxiliary process that aims to reconstruct the power flow equations at the stability threshold that defines the load margin. In the PGNN training stage, there are two error functions that must be minimized, one related to the empirical knowledge of the system and the other related to the physical knowledge of the system associated with the guarantee of power flow. Case studies are presented for two test power systems, IEEE 68-bus system and Brazilian interconnected power system, considering a set of operating conditions, signal noise and outliers. The results show the superior performance of PGNN over traditional NNs.

Online Monitoring of Oscillation Modes for Small-Signal Security Assessment

This paper presents the main concepts of small-signal security assessment of power systems, focusing on the online monitoring of small-signal stability. In this work, a computational tool for online monitoring of oscillation modes (OLMO) in power systems is proposed. The objective of the methodology consists of determining small-signal stability margins, security levels, and corrective measures that can improve the damping factor of oscillation modes present in such systems. These corrective measures can be associated with possible redispatches of power plants. In this context, the Closest Security Boundary for Generation Redispatch using Eigenvalue Sensitivities, known as CSBGRES Method, could be applied to determine an optimized redispatch for the power plants of the system to ensure a desired minimum damping factor, previously chosen, for oscillation modes. The proposed tool can be applied for controlling power plant dispatches on Smart Grids, focusing on the damping factor of oscillation modes. A small test system is used to evaluate the proposed computational feature and the results were presented to demonstrate its importance to power system security. Results of the utilization of OLMO on the Brazilian Interconnected Power System (BIPS) are presented to prove the viability of using this method in large-scale power systems.

Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system

This paper focuses on the development of shallow and deep neural networks in the form of multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short-term load forecasting problem. Different model architectures are tested, and global climate model information is used as input to generate more accurate forecasts. A real study case is presented for the Brazilian interconnected power system and the results generated are compared with the forecasts from the Brazilian Independent System Operator model. In general terms, results show that the bidirectional versions of long-short term memory and gated recurrent unit produce better and more reliable predictions than the other models. From the obtained results, the recurrent neural networks reach Nash-Sutcliffe values up to 0.98, and mean absolute percentile error values of 1.18%, superior than the results obtained by the Independent System Operator models (0.94 and 2.01% respectively). The better performance of the neural network models is confirmed under the Diebold-Mariano pairwise comparison test.

Impact of Cascade Disconnection of Distributed Energy Resources on Bulk Power System Stability: Modeling and Mitigation Requirements

This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources (DERs) with focus on bulk power system stability. The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations. The proposed approach concentrates on the rate-of-change of frequency (RoCoF) protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability. Initially, the impact of disconnection of DERs on the Brazilian Interconnected Power System (BIPS) stability is analyzed, highlighting the importance of modeling such disconnection in electromechanical stability studies, even considering low penetration levels of DERs. Then, the proposed approach is applied to the BIPS, evidencing its benefits when specifying the minimum requirements of anti-islanding protection, without overestimating them.

WAMS-based two-level robust detection methodology of power system events

With the advance of Wide-Area Measurement Systems (WAMS), power system operators have direct access to a large amount of data with valuable information about the power system dynamic performance. As a result, there is a clear need for new data-driven methodologies capable of extracting relevant information from this collected data. One of the key challenges is correctly detecting power system disturbances to avoid false alarms during real-time operation as well as off-line disturbance analysis. This paper proposes a two-level robust event detection methodology aiming to reduce false disturbance detection (false positives/alarms) and validate true events. The methodology is divided into two-levels: (i) signal processing analysis (ii) deep neural network (DNN) classification. In the first level, we apply a widely used spectral analysis based on the Discrete Wavelet Transform (DWT) to event detection. In the second level, the events detected by the DWT are processed by a DNN to check if they are real events or false alarms. Finally, the proposed methodology is evaluated using real synchrophasor event records from the Brazilian Interconnected Power System (BIPS).

Time-Domain Differential Protection of Transmission Lines Based on Bayesian Inference

This paper presents a time-domain differential protection method based on the Bayes’ Theorem and on the current samples to determine the fault occurrence probability as a function of time. It also describes the theory involved in developing the proposed protection method when considering the differential protection of transmission lines. The authors performed fault simulations using an existing electrical system modeled in EMTP varying the fault type, phases involved, fault resistance, inception angle, and fault location, and carried out a sensitivity analysis regarding high impedance faults, current transformer saturation, noisy data, misalignments between terminal samples and power swings. The authors also tested the proposed method against actual fault events from four existing transmission lines of the Brazilian interconnected power system. All results indicate that the proposed solution is fast, accurate, secure, and dependable.

Impact analysis of COVID-19 pandemic on the electricity demand, frequency control and electromechanical oscillation modes of the Brazilian Interconnected Power System using low voltage WAMS data

COVID-19 pandemic presented unique features among the range of threats encountered over the last century. Its impact echoes throughout the world affecting societies and their patterns of behavior, hence affecting the usage of electricity and the operation of power systems. This paper provides an analysis of the impact of COVID-19 Pandemic on the electricity demand, frequency control and electromechanical oscillation modes of the Brazilian Interconnected Power System (BIPS), taking into account public data disclosed by the Brazilian Independent System Operator (BISO) and data acquired through the MedFasee Project, the Brazilian low voltage wide area monitoring system (WAMS) leaded by the Federal University of Santa Catarina. Main results indicate that the BISO has been successful on controlling the system frequency and the main electromechanical interarea modes, despite the occurrence of a significant demand reduction in the BIPS in a certain period of time due to the COVID-19 pandemic. The total time of operation in underfrequency or overfrequency registered during the months with most demand reduction is at most 20% lower than the maximum time registered in the other months studied. The damping of the modes observed in the months with demand reduction has not reached values lower than 10% and the frequencies of oscillation have varied in a range of 0.05Hz, in agreement to what has been observed in other months.

Multi‐year two-stage generation and transmission expansion planning: intermittent renewable energy sources integration for Brazilian interconnected power system

Multi‐year two-stage generation and transmission expansion planning: intermittent renewable energy sources integration for Brazilian interconnected power system

Computationally Efficient Optimal Control for Unstable Power System Models

In this article, the focus is mainly on gaining the optimal control for the unstable power system models and stabilizing them through the Riccati-based feedback stabilization process with sparsity-preserving techniques. We are to find the solution of the Continuous-time Algebraic Riccati Equations (CAREs) governed from the unstable power system models derived from the Brazilian Inter-Connected Power System (BIPS) models, which are large-scale sparse index-1 descriptor systems. We propose the projection-based Rational Krylov Subspace Method (RKSM) for the iterative computation of the solution of the CAREs. The novelties of RKSM are sparsity-preserving computations and the implementation of time-convenient adaptive shift parameters. We modify the Low-Rank Cholesky-Factor integrated Alternating Direction Implicit (LRCF-ADI) technique-based nested iterative Kleinman–Newton (KN) method to a sparse form and adjust this to solve the desired CAREs. We compare the results achieved by the Kleinman–Newton method with that of using the RKSM. The applicability and adaptability of the proposed techniques are justified numerically with MATLAB simulations. Transient behaviors of the target models are investigated for comparative analysis through the tabular and graphical approaches.

Performance analysis of a DNN classifier for power system events using an interpretability method

Nowadays, there is a clear need for Machine Learning methods capable of extracting relevant and reliable information from synchrophasor data. In this paper, the application of an explainable data-driven method is carried out in order to inspect the performance of DNN classifier for event identification using synchrophasor measurements. The DNN classifier is the Long–Short Term Memory (LSTM) which is suitable for the extraction of dynamic features. The key advantage of this approach is the use of an interpretability inspection named SHAP (SHapley Additive exPlanation) values, based on cooperative game theory (Shapley values), which provide the means to evaluate the predictions of the LSTM and detecting possible bias. The main contributions are stated as follows: (i) it explains how the LSTM classifier is making its decisions; (ii) it helps the designer to improve the training of the classifier; (iii) certify that the resulting classifier has a consistent and coherent performance according to domain knowledge of the problem; (iv) when the user understands that the classifier is making coherent decisions, it clearly reduces the concerns of the application of DNN methods in critical infrastructure. Additionally, the proposed approach is evaluated using real synchrophasor event records from the Brazilian Interconnected Power System (BIPS).

Clustered R3LS: A novel approach for online estimation of power system dominant dynamics

Estimating electromechanical oscillations of power systems plays a crucial role to infer about their stability. In this paper it is proposed a new method in which dominant dynamics of power systems are inferred by analyzing pole estimates produced by several regularized robust recursive least squares (R3LS) implementations operating individually — each one with a different set of autoregressive moving average exogenous (ARMAX) model orders. The proposed method is named “Clustered R3LS”, since it properly adapts the so-called K-means clustering algorithm to automatically model regions of pole agglomeration produced by these individual R3LS implementations. An additional strategy for ignoring (discarding) poles identified as spurious is also part of the proposed Clustered R3LS algorithm. From a practical point of view, Clustered R3LS is here shown to more effectively track dominant dynamics of power systems when compared to Classical R3LS, which is based on analyzing only pole estimates produced by a single R3LS implementation operating with a fixed set of ARMAX model orders that is usually hard to tune. Three case studies are here used to validate the proposed Clustered R3LS method for both ambient and ringdown data. The first and second case studies are based on two well known benchmark models proposed for the analysis of oscillatory dynamics in power systems, whereas the third case study considers actual power system data extracted during a large-scale event from the Brazilian Interconnected Power (BIP) system.

Small-signal stability assessment of power systems with multiple transport delays based on s-domain modelling

Wide area monitoring systems are becoming increasingly important to power systems security. Control and protection schemes that use remote sensing are being developed and becoming a reality. The delay introduced by remote sensing should be accurately modelled in the analysis tools. A precise representation of transport delays turns the system model into an infinite-dimensional system, with an infinite number of poles. As a result, usual representations such as state-space and descriptor systems become unfeasible. This paper presents novel mathematical advances and computational methodologies to allow a comprehensive modal analysis using exact representation of multiple transport delays. The proposed approach is based on a non-linear formulation in the Laplace domain and uses numerical methods founded on the general complex variable theory. The proposed methodologies are analysed through simulations in a didactical example and in the Brazilian Interconnected Power System.

Phasor-domain Dynamic Model of Asymmetric Current Injection Controller for Converter-interfaced Generator

This paper presents a controller model of asymmetric current injection for converter-interfaced generators suitable for root-mean-square (RMS) phasor-domain, fundamental-frequency, three-phase, and dynamic simulation tools. The effectiveness of the proposed controller is assessed with simulations in test systems with high percentage of converter-interfaced generation. The simulations focus on the operation of protection relays that use negative-sequence quantities in their directional elements. This paper also presents and compares two strategies to limit reactive negative-sequence currents, and active and reactive positive-sequence currents. A tutorial test system and a regional system part of the actual Brazilian Interconnected Power System are used to assess the correctness of the proposed controller in three-phase fundamental-frequency RMS dynamic simulations.

Dynamic security regions in power systems with active overexcitation limiters

This paper analyzes the impact of active overexcitation limiters (OELs) on the dynamic security assessment of power systems, with focus on transient stability. The analysis is concentrated on dynamic security regions evaluation, considering different OELs types, including the takeover and summed ones. Initially, the importance of the OEL modelling is exemplified in a study carried out on the Brazilian Interconnected Power System, where the OEL activation results in a significant transfer capability limitation. After that, the work explores the development of dynamic security regions in the IEEE New England system, showing how these regions can be distorted depending on the OELs representation.

Risk neutral reformulation approach to risk averse stochastic programming

The aim of this paper is to show that in some cases risk averse multistage stochastic programming problems can be reformulated in a form of risk neutral setting. This is achieved by a change of the reference probability measure making “bad” (extreme) scenarios more frequent. As a numerical example we demonstrate advantages of such change-of-measure approach applied to the Brazilian Interconnected Power System operation planning problem.

Synthetic scenario generation of monthly streamflows conditioned to the El Niño–Southern Oscillation: application to operation planning of hydrothermal systems

The Brazilian Interconnected Power System is hydro dominated and characterized by large reservoirs presenting multi-year regulation capability, arranged in complex cascades over several river basins. In this way, the expansion and operation planning should take into account the uncertainties about the future inflows to hydroplants reservoirs. Currently, a stochastic model for synthetic scenarios generation of monthly streamflow, based on Periodic Auto-Regressive formulation, is used to address the uncertainty. This is the official model used in the Brazilian energy operation planning by the Ministry of Mines and Energy, the National Operator of Electrical System, the Chamber of Electric Energy Commercialization and the Energy Planning Company. Recently, a great scientific effort has been made to include relevant climatic information in stochastic streamflow models. Among several important climatic phenomena in the Brazilian hydrological cycles, El Nino–Southern Oscillation has been pointed as one of the most important. Although the stochastic models that include exogenous variables or that use wavelets present good results, they have limitations for long-term horizon projections or are not suitable for applications that use stochastic dual dynamic programming, which is the case of the Brazilian electrical system. This work proposes an improvement to the current scenario generation model, in order to consider the climate information, but still being suitable to be applied in SDDP algorithms. To achieve this goal, a Markov-Switching Periodic Auto-Regressive model is presented. It is demonstrated that the methodology is able to generate synthetic scenarios which better resembles the observed streamflow, mainly during periods when the streamflow are below-average.

PMU-WAMS research and application in Brazil

Wide area measurement system (WAMS), which is based on synchronization data from phasor measurement units (PMU) and EMS SCADA, is implemented to establish a system model that can handle certain functions such as realtime power system monitoring, oscillation mode analysis, accident analysis and decision-making assistance for emergency control. The Brazilian Interconnected Power System (BIPS) is a large system covering an extensive geographical region, which faces certain risks and challenges. It has several main transmission corridors associated with large power plants and interconnection between the northern and southeastern regions. Mismatch between the energy base and load pool also exists in Brazil as energy resources are not well-distributed; therefore, the use of large-capacity, long-distance transmission technique to transmit remote power is unescapable. On the other hand, there are many types of voltage levels and multiple entangled electromagnetic loops owing to historical reasons. Then, for insufficient power reservation and defective grid body in load pools, once the external power is cut, it’s easy to raise a blackout. The infrastructure is old and the power system operates close to the upper limit. All these represent risks and challenges to BIPS. Through WAMS technology research method in this project, the electrical power system function of monitoring, analysis, and control improved from the static state to the dynamic state. WAMS enhances data integration and real-time analysis capabilities, and can provide dispatchers with high quality real-time dynamic information and decision-making support information, enhance monitoring of auxiliary services in the electricity market, enable operators to improve the accuracy of power network analysis, thereby increasing power grid monitoring and operation, and improve the transmission capacity and reliability of the power grid operation [1].

Optimized coordination of transmission network outages in interconnected power grids

This paper addresses an interesting problem for independent system operators (ISOs): the coordination of network maintenance schedule. Requested outages should be coordinated by the ISO to avoid unacceptable operating conditions and to perform the system risk evaluation, which requires the analysis of many operating scenarios to accommodate the planned outages. A methodology to solve this combinatorial problem is developed, based on an evolutionary algorithm that comprises strategies to control the diversification and intensification phases of the optimization process. Numerical results obtained with simulation studies, performed on four IEEE test-systems and the reduced southern Brazilian interconnected power system, are discussed. It is shown that the proposed approach can effectively lead to high-quality solutions in a reasonable computing time for short-term operation planning.