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Venkataramana Ajjarapu

Publications and source records attributed to Venkataramana Ajjarapu.

At least 19 recordsLinked to original sources

Criteria-Aware EMT-Based Short-Term Voltage Performance Index for Dynamic Assessment of Inverter-Dominated Power Systems

The increasing penetration of inverter-based resources (IBRs) into bulk power systems has fundamentally altered short-term voltage dynamics following disturbances. Conventional short-circuit capacity (SCC) metrics provide a useful screening indicator of grid strength but are unable to fully capture post-disturbance voltage behavior at buses with dynamic loads, converter controls, or protection interactions. A bus with high SCC may still experience deep voltage dips, delayed recovery, or transient overvoltage that violates operating criteria. This paper proposes the Short-Term Voltage Performance Index (STVPI), an electromagnetic-transient (EMT)-based, criteria-aware metric that quantifies the quality of the post-disturbance voltage waveform relative to user-defined performance limits. STVPI processes voltage signals at the half-cycle level by computing a weighted log-amplitude ratio between the actual waveform and an ideal half-sine reference. Monotonic recovery envelopes on the overvoltage and undervoltage sides are compared against half-normal reference distributions using Kullback--Leibler (KL) divergence, normalized by the KL divergence of the critical voltage envelope, yielding two directional indices -- $\mathrm{STVPI}^{+}$ and $\mathrm{STVPI}^{-}$ -- whose combination produces a baseline-corrected scalar severity score. Bus-level and event-level aggregation derive BSTVPI and ESTVPI, enabling simultaneous identification of dynamically weak buses and critical fault contingencies. The framework is validated on the IEEE 9-bus and 39-bus test systems with IBR integration.

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Online Long-Term Voltage Stability Margin Estimation for IBR/DER Dominated Power System with Integrated VSM-Aware TSO-DSO Framework

The rapid growth of inverter-based resources (IBRs) and distributed energy resources (DERs) has fundamentally altered the long-term voltage stability characteristics of modern power systems. This article leverages the advantages of machine learning (ML) for the online estimation of long-term voltage stability margin (VSM) and enhancement of VSM through coordinated transmission system operator-distribution system operator (TSO-DSO) optimization. An explicit analytical VSM expression is derived from offline T&D co-simulation data using a physics-informed ML-trained model under probabilistic loading and generation mix scenarios, while accounting for unbalanced distribution modeling. The resulting closed-form VSM representation is linearized and embedded into the TSO optimization problem, enabling real-time enforcement of minimum VSM constraints. We further enhance operational efficiency by incorporating VSM sensitivities into both transmission and distribution optimization, allowing prioritization of the most influential reactive power resources. Simulation studies conducted on the IEEE 30-bus transmission network integrated with multiple IEEE 37-node distribution feeders validate that the proposed framework successfully achieves the desired VSM enhancement while maintaining high estimation accuracy.

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Consensus Clustering for the Identification of Coherent Regions with Varied Generation Mix

With a steady increase in the inverter technology integration to the grid, frequency response of the large inter-connection system becomes more unpredictable. This leads to a significant change in the boundaries of the coherent region, which highly depends on the changing disturbance locations and operating conditions. While most of the existing coherency identification is based on a single large generator outage, it is important to identify these boundaries in view of wide range of disturbances. With large amount of inverters in the system, there is increase in the dynamic interactions of the various grid components leading to a need for such boundary identification. This paper presents the multi-view consensus algorithm to identify coherency in the case of variable grid operating conditions and wide range of disturbances. The proposed approach is demonstrated by identifying the coherent regions in the miniWECC 240 bus test system.

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Trajectory-Based Nonlinear Indices for Real-Time Monitoring and Quantification of Short-Term Voltage Stability

Existing short term voltage stability (STVS) methods typically address either voltage oscillations or delayed voltage recovery; however, the coexistence of both phenomena has not been adequately covered in the literature. Moreover, existing real-time STVS assessment methods often provide only binary stability classifications. This paper proposes novel indices that enable early detection and quantify the degree of stability. The proposed method decomposes post-fault voltage trajectories using Empirical Mode Decomposition (EMD) into residual and oscillatory components. It then employs Lyapunov Exponents (LEs) to characterize the dynamic behavior of each component and evaluates the stability degree using Kullback Leibler (KL) divergence by comparing the LEs of each component with those of a predefined critical signal. The proposed indices assess oscillatory stability significantly faster than the traditional LE method applied directly to the original signal. Specifically, they detect stability within 0.6 seconds after a fault, compared to approximately 10 seconds for the conventional LE approach. In addition, the delayed-recovery index can identify generator trips caused by over-excitation limits within 3 seconds, well before the actual trip occurs at approximately 20 seconds, thereby providing operators and controllers sufficient time to take preventive actions. Furthermore, thresholds are derived to distinguish between stable and unstable cases, offering a graded measure of the stability margin. Simulation studies on the Nordic test system under varying load conditions demonstrate the effectiveness of the proposed indices.

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TSO-DSO Coordinated Reactive Power Dispatch for Smart Inverters with Multiple Control Modes Real-Time Implementation

This paper presents TSO-DSO coordinated reactive power dispatch, with a focus on real-time implementation. A sensitivity-aware, mixed-integer linear programming (MILP) formulation is developed to model the IEEE 1547-compliant droop-based control modes Volt VAR (VV), Volt Watt (VW), and Watt VAR (WV) of smart inverters. The algorithm employs a hierarchical optimization strategy using Special Ordered Sets (SOS1) to enhance computational efficiency and supports limited measurement scenarios through Recursive Least Squares (RLS) estimation. The proposed method is tested on the IEEE 13-bus and 123-bus distribution networks, which are connected to a 9-bus transmission system. Results demonstrate the feasibility and effectiveness of the real-time dispatch framework in improving voltage regulation and minimizing power curtailment.

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Novel Data-Driven Indices for Early Detection and Quantification of Short-Term Voltage Instability from Voltage Trajectories

This paper presents a novel Short-Term Voltage Stability Index (STVSI), which leverages Lyapunov Exponent-based detection to assess and quantify short-term stability triggered by Over Excitation Limiters (OELs) or undamped oscillations in voltage. The proposed method is measurement-based and decomposes the voltage trajectory into two key components using Empirical Mode Decomposition (EMD): a residual part, which indicates delayed voltage recovery, and an oscillatory part, which captures oscillations. The residual component is critical, as it can detect activation of OELs in synchronous generators or Low Voltage Ride-Through (LVRT) relays in inverter-based resources, potentially leading to instability within the quasisteady-state time frame. Meanwhile, the oscillatory component may indicate either a stable or unstable state in the short term. To accurately assess stability, STVSI employs an entropy-based metric to measure the proximity of the system to instability, with specific indices for short-term voltage stability based on oscillations and recovery. Simulations on the Nordic power system demonstrate that STVSI effectively identifies and categorizes voltage stability issues. Moreover, STVSI not only detects voltage stability conditions but also qualitatively assesses the extent of stability, providing a nuanced measure of stability.

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Extended Sensitivity-Aware Reactive Power Dispatch Algorithm for Smart Inverters with Multiple Control Modes

The increasing integration of Distributed Energy Resources (DERs) in distribution networks presents new challenges for voltage regulation and reactive power support. This paper extends a sensitivity-aware reactive power dispatch algorithm tailored to manage smart inverters operating under different control modes, including PQ, PV, and Volt-Var (VV). The proposed approach dynamically optimizes reactive power dispatch and voltage setpoints, enabling effective coordination among distribution systems as a virtual power plant (VPP) to support the transmission network. The algorithm is applied to the IEEE 13-bus and IEEE-123 bus test systems, and its performance is validated by comparing results with OpenDSS simulations across various operating scenarios. Results show that the maximum error in the voltages is less than 0.015 pu.

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Enhanced Entropy-Based Metric for Characterization of Delayed Voltage Recovery

Ensuring accurate violation detection in power systems is paramount for operational reliability. This paper introduces an enhanced voltage recovery violation index (EVRVI), a comprehensive index designed to quantify fault-induced delayed voltage recovery (FIDVR). EVRVI enhances traditional entropy-based methods by leveraging Empirical Mode Decomposition (EMD) to extract key features from the voltage signal, which are then used to quantify over-voltage (OV) and under-voltage (UV) events. Our simulations on the Nordic system, involving over 245k scenarios, demonstrate EVRVI's superior ability to identify and categorize voltage recovery issues compared to the traditional entropy-based measure. EVRVI not only significantly reduces false negatives in violation detection but also provides a reliable framework for over-voltage detection, making it an invaluable tool for modern power system studies.

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Robust VAR Capability Curve of DER with Uncertain Renewable Generation

Active distribution system with high penetration of inverter based distributed energy resources (DER) can be utilized for VAR-related ancillary services. To utilize the DER flexibility, transmission system operator (TSO) must be presented with the aggregated DER flexibility of distribution system. However, the uncertainty in renewable generation questions the credibility of aggregated capability curve in practice. In this paper, we incorporate the uncertainty into aggregation process to develop a robust capability curve while preserving the real physics (unbalance and lossy nature) of distribution system. Statistical inference method is employed to quantify uncertainty in solar generation and quantified uncertainty is integrated into a chance constrained optimal power flow (OPF). It provides the grid operator with the dispatchable aggregated reactive power capability. The resulting capability curve with the associated probability can be harnessed by the TSO for decision making for both planning and operation.

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Deriving DERs VAR-Capability Curve at TSO-DSO Interface to Provide Grid Services

The multitudes of inverter-based distributed energy resources (DERs) can be envisioned as distributed reactive power (var) devices (\textit{mini-SVCs}) that can offer var flexibility at TSO-DSO interface. To facilitate this vision, a systematic methodology is proposed to derive an aggregated var capability curve of a distribution system with DERs at the substation level, analogous to a conventional bulk generator. Since such capability curve will be contingent to the operating conditions and network constraints, an optimal power flow (OPF) based approach is proposed that takes inverter headroom flexibility, unbalanced nature of system and coupling with grid side voltage into account along with changing operating conditions. Further, the influence of several factors such as compliance to IEEE 1547 on the capability curve is thoroughly investigated on an IEEE 37 bus and 123 bus distribution test system along with unbalanced DER proliferation. Validation with nonlinear analysis is presented along with demonstration of a scenario with T-D co-simulation.

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Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading

This paper describes how domain knowledge of power system operators can be integrated into reinforcement learning (RL) frameworks to effectively learn agents that control the grid's topology to prevent thermal cascading. Typical RL-based topology controllers fail to perform well due to the large search/optimization space. Here, we propose an actor-critic-based agent to address the problem's combinatorial nature and train the agent using the RL environment developed by RTE, the French TSO. To address the challenge of the large optimization space, a curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using network physics for enhanced agent learning. Further, a parallel training approach on multiple scenarios is employed to avoid biasing the agent to a few scenarios and make it robust to the natural variability in grid operations. Without these modifications to the training procedure, the RL agent failed for most test scenarios, illustrating the importance of properly integrating domain knowledge of physical systems for real-world RL learning. The agent was tested by RTE for the 2019 learning to run the power network challenge and was awarded the 2nd place in accuracy and 1st place in speed. The developed code is open-sourced for public use.

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Monitoring & Mitigation of Delayed Voltage Recovery using μPMU Measurements with Reduced Distribution System Model

This paper proposes a new method to monitor and mitigate fault induced delayed voltage recovery (FIDVR) phenomenon in distribution systems using μPMU measurements in conjunction with a Reduced Distribution System Model (RDSM). The recovery time estimated from a dynamic analysis of the FIDVR is used to monitor its behavior and a linear optimization is formulated to control air conditioner loads and DER reactive power injection to mitigate the FIDVR severity. The RDSM is made up of several sub-models, each of which is analogous to the Composite Load Model (CLM) with selected parameters. The linear formulation in combination with the RDSM reduces the computation time, enabling online execution. Simulated μPMU measurements from the IEEE 37 node distribution system connected to the IEEE 9 bus system under various fault scenarios are used to evaluate the proposed methodology. The resulting mitigation schemes are validated using combined transmission-distribution system simulations, thereby demonstrating that μPMU measurements along with the RDSM enable FIDVR mitigation by optimal control of reactive power injection from DERs with minimal load disconnection

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Information Based Data-Driven Characterization of Stability and Influence in Power Systems

Stability analysis of a power network and its characterization (voltage or angle) is an important problem in the power system community. However, these problems are mostly studied using linearized models and participation factor analysis. In this paper, we provide a purely data-driven technique for small-signal stability classification (voltage or angle stability) and influence characterization for a power network. In particular, we use Koopman operator framework for data-driven discovery of the underlying power system dynamics and then leverage the newly developed concept of information transfer for discovering the causal structure. We further use it to not only identify the influential states (subspaces) in a power network, but also to clearly characterize and classify angle and voltage instabilities. We demonstrate the efficacy of the proposed framework on two different systems, namely the 3-bus system, where we reproduce the already known results regarding the types of instabilities, and the IEEE 9-bus system where we identify the influential generators and also the generator (and its states) which contribute to the system instability, thus identifying the type of instability.

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Data-driven Identification of Nonlinear Power System Dynamics Using Output-only Measurements

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements.

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Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems

In this paper, we propose a data-driven approach for uncertainty propagation and reachability analysis in a dynamical system. The proposed approach relies on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F) and Koopman operators. The uncertainty can be characterized in terms of the moments of a probability density function. We demonstrate how the P-F and Koopman operators are used for propagating the moments. Time-series data is used for the finite-dimensional approximation of the linear operators, thereby enabling data-driven approach for moment propagation. Simulation results are presented to demonstrate the effectiveness of the proposed method.

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Counterintuitive VSM Behavior under CVR Incorporating Distribution System

This paper analyses the impact of conservation by voltage reduction (CVR) on voltage stability margin (VSM) considering transmission and distribution (T&D) systems. VSM is determined by P-V curve analysis using PSSE and GridLAB-D solvers to co-simulate the T&D systems under CVR and No CVR conditions. ZIP loads with profile [ZIP] = [0.4 0.3 0.3] are used to model the load. The paper discusses the counterintuitive result: under CVR, the VSM is reduced. Theoretical justification for the reduced VSM under CVR is the increase in the effective impedance between generation and load and this is proved using an extended 2-bus system. The paper shares T&D co-simulation results with IEEE 9-bus transmission system and a larger 123-bus distribution system and with distributed generation (DG) in unity power factor (UPF) and volt-VAR control (VVC) mode.

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Investigation of Relevant Distribution System Representation with DG for Voltage Stability Margin Assessment

This paper emphasizes the importance of including the unbalance in the distribution networks for stability studies in power systems. The paper aims to: discuss the various simulation methods for power system analysis; highlight the need for modeling unbalanced distribution system for accurate load margin assessment; demonstrate the influence of net-load unbalance (NLU) on voltage stability margin (VSM). We also share a T&D co-simulation interface with commercial power system solvers. The distribution system is evolving rapidly with high proliferation of distributed energy resources (DERs); these are not guaranteed to proliferate in a balanced manner and uncertainty resulting due to these DERs is well acknowledged. These uncertainties cannot be captured or visualized without representing the distribution system in detail along with the transmission system. We show the impact of proliferation of DERs in various 3-phase proportions on voltage stability margin through T&D co-simulation. We also study the impact of volt/VAR control on voltage stability margin. This analysis is only possible by representing the distribution system in detail through T&D co-simulation. Higher percentage of net-load unbalance (NLU) in distribution system aggravates the stability margin of the distribution system which can further negatively impact the overall stability margin of the system.

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Real-Time Local Volt/VAR Control Under External Disturbances with High PV Penetration

Volt/var control (VVC) of smart PV inverter is becoming one of the most popular solutions to address the voltage challenges associated with high PV penetration. This work focuses on the local droop VVC recommended by the grid integration standards IEEE1547, rule21 and addresses their major challenges i.e. appropriate parameters selection under changing conditions, and the control being vulnerable to instability (or voltage oscillations) and significant steady state error (SSE). This is achieved by proposing a two-layer local real-time adaptive VVC that has two major features i.e. a) it is able to ensure both low SSE and control stability simultaneously without compromising either, and b) it dynamically adapts its parameters to ensure good performance in a wide range of external disturbances such as sudden cloud cover, cloud intermittency, and substation voltage changes. A theoretical analysis and convergence proof of the proposed control is also discussed. The proposed control is implementation friendly as it fits well within the integration standard framework and depends only on the local bus information. The performance is compared with the existing droop VVC methods in several scenarios on a large unbalanced 3-phase feeder with detailed secondary side modeling.

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