Real-time Grid Research Articles

A hybrid physical and co-simulation modern adaptive power protection testbed for testing the resilience of smart grids under cyber-physical threats

Power protection systems play a critical role in ensuring the safe and reliable operation of modern power grids. With the increasing complexity of grid topologies and the integration of Distributed Energy Resources (DERs), traditional protection schemes face challenges in maintaining effective coordination among relay systems. This paper presents a new adaptive Overcurrent Relay (OCR) protection and investigates the resilience of different traditional and new adaptive OCR protection schemes against various cyber-physical threats in a smart grid environment. Key contributions include the implementation of a novel adaptive protection scheme designed to dynamically adjust OCR settings based on real-time grid conditions, improving flexibility and responsiveness in managing grid variations induced by DER integration and operational changes. The proposed scheme is validated and implemented using the Multifunction Protection Relay SIEMENS 7SJ62, following to the programming standards of IEC 61131–3, ensuring the reliability and practical applicability of the adaptive OCR scheme in real-world scenarios. Additionally, a dedicated testbed is developed to simulate real-time cyber-physical interactions, incorporating hardware components such as the SIEMENS 7SJ62 and OMICRON-256 test device, along with high-performance computers and communication networks to facilitate comprehensive evaluations of adaptive OCR schemes under varying operational conditions. The study also examines the implications of real-time cyber-attacks on power system operations and the effectiveness of adaptive OCR protection schemes, addressing cybersecurity concerns and proposing mitigation strategies to the system resilience.

Online EVs Vehicle-to-Grid Scheduling Coordinated with Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to operational constraints. However, scheduling V2G strategies online poses challenges due to uncertainties such as electricity prices and EV arrival/departure patterns. To address this, we propose an online V2G scheduling framework based on deep reinforcement learning (DRL) to optimize EV battery utilization in microgrids with different energy sources. Firstly, our approach proposes an online scheduling model that integrates the management of V2G and multi-energy flexible demands, modeled as a Markov Decision Process (MDP) with an unknown transition. Secondly, a DRL-based Soft Actor-Critic (SAC) algorithm is utilized to efficiently train neural networks and dynamically schedule EV charging and discharging activities in response to real-time grid conditions and energy demand patterns. Extensive simulations are conducted in case studies to testify to the effectiveness of our proposed approach. The overall results validate the efficacy of the DRL-based online V2G scheduling framework, highlighting its potential to drive profitability and sustainability in multi-energy microgrid operations.

Real-time grid detection in sheet metal fiber laser cutting through coaxial monitoring

Real-time defects’ estimation and control of the cut quality through the coaxial camera monitoring of the kerf are amongst the most promising developments for laser cutting. In industrial systems, sheets are positioned on a metallic grid creating discontinuities in the cutting process due to unpredictable thickness, blown and resolidified material and the time varying position of the grid. The interaction between laser radiation escaping from the kerf and the grid, along with the difficult outflow of molten material, causes changes in the process emission images. These changes influence the defect estimation approach, and, consequently, the control action. In this work, a real-time grid identification and classification-based machine learning algorithm was developed and tested during the fusion cutting of 6 mm thick Al5754, exploiting a NIR coaxial monitoring system. Real-time control experiments with dross estimates were performed, demonstrating a correct identification of the grid and highlighting a feasible industrial application.

Distributed Generation Control Using Ripple Signaling and a Multiprotocol Communication Embedded Device

Remotely performing real-time distributed generation control and a demand response is a basic aspect of the grid ancillary services provided by grid operators, both the transmission grid operators (TSOs) and distribution grid operators (DNOs), in order to ensure that voltage, frequency and power loads of the grid remain within safe limits. The stochastic production of electrical power to the grid from the distributed generators (DGs) from renewable energy sources (RES) in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the efforts of the DNOs to keep the grid’s operation within safe limits and prevent cascading blackouts while staying in compliance with the SAIDI and SAIFI indices during repair and maintenance operations. Also taking into consideration the aging of the existing grid infrastructure, and making it more prone to failure year by year, it is yet of great significance for the DNOs to have access to real-time feedback from the grid’s infrastructure—which is fast, has low-cost upgrade interventions, is easily deployed on the field and has a fast response potential—in order to be able to perform real-time grid management (RTGM). In this article, we present the development and deployment of a control system for DG units, with the potential to be installed easily to TSO’s and DNO’s substations, RES plants and consumers (i.e., charging stations of electric vehicles). This system supports a hybrid control mechanism, either via ripple signaling or through a network, with the latter providing real-time communication capabilities. The system can be easily installed on the electric components of the grid and can act as a gateway between the different vendors communication protocols of the installed electrical equipment. More specifically, a commercially available, low-cost board (Raspberry Pi) and a ripple control receiver are installed at the substation of a PV plant. The board communicates in real-time with a remote server (decision center) via a 5G modem and with the PV plants inverters via the Modbus protocol, which acquires energy production data and controls the output power of each inverter, while one of its digital inputs can be triggered by the ripple control receiver. The ripple control receiver receives on-demand signals with the HEDNO, triggering the digital input on the board. When the input is triggered, the board performs a predefined control command (i.e., lower the inverter’s power output to 50%). The board can also receive control commands directly from the remote server. The remote server receives real-time feedback of the acquired inverter data, the control signals from the ripple control receiver and the state and outcome of each performed control command.

A comparative evaluation of Stacked Auto-Encoder neural network and Multi-Layer Extreme Learning Machine for detection and classification of faults in transmission lines using WAMS data

Smart grid is envisaged as a power grid that is extremely reliable and flexible. The electrical grid has wide-area measuring devices like Phasor measurement units (PMUs) deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability. The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data. This research proposes a transmission line fault detection and classification (FD&C) system based on an auto-encoder neural network. A comparison between a Multi-Layer Extreme Learning Machine (ML-ELM) network model and a Stacked Auto-Encoder neural network (SAE) is made. Additionally, the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models. With substantially shorter testing time, the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79% accuracy. The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms. To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio (SNR) ranging from 10 dB to 40 dB. The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.

Real-Time Grid Monitoring and Protection: A Comprehensive Survey on the Advantages of Phasor Measurement Units

The emerging smart-grid and microgrid concept implementation into the conventional power system brings complexity due to the incorporation of various renewable energy sources and non-linear inverter-based devices. The occurrence of frequent power outages may have a significant negative impact on a nation’s economic, societal, and fiscal standing. As a result, it is essential to employ sophisticated monitoring and measuring technology. Implementing phasor measurement units (PMUs) in modern power systems brings about substantial improvement and beneficial solutions, mainly to protection issues and challenges. PMU-assisted state estimation, phase angle monitoring, power oscillation monitoring, voltage stability monitoring, fault detection, and cyberattack identification are a few prominent applications. Although substantial research has been carried out on the aspects of PMU applications to power system protection, it can be evolved from its current infancy stage and become an open domain of research to achieve further improvements and novel approaches. The three principal objectives are emphasized in this review. The first objective is to present all the methods on the synchro-phasor-based PMU application to estimate the power system states and dynamic phenomena in frequent time intervals to observe centrally, which helps to make appropriate decisions for better protection. The second is to discuss and analyze the post-disturbance scenarios adopted through better protection schemes based on accurate and synchronized measurements through GPS synchronization. Thirdly, this review summarizes current research on PMU applications for power system protection, showcasing innovative breakthroughs, addressing existing challenges, and highlighting areas for future research to enhance system resilience against catastrophic events.

Real-Time Grid Management: Keeping the Lights On!

The objectives of real-time grid management (RTGM) are to prevent widespread blackouts, ensure the grid is operating within safe limits, and to basically “keep the lights on” for all customers.

Wide area monitoring system operations in modern power grids: A median regression function-based state estimation approach towards cyber attacks

Modern power grid is a generation mix of conventional generation facilities and variable renewable energy resources (VRES). The complexity of such a power grid with generation mix has routed the utilization of infrastructures involving phasor measurement units (PMUs). This is to have access to real-time grid information. However, the traffic of digital information and communication is potentially vulnerable to data-injection and cyber attacks. To address this issue, a median regression function (MRF)-based state estimation is presented in this paper. The algorithm was stationed at each monitoring node using interacting multiple model (IMM)-based fusion architecture. An exogenous variable-driven representation of the state is considered for the system. A mapping function-based initial regression analysis is made to depict the margins of state estimate in the presence of data-injection. A median regression function is built on top of it while generating and evaluating the residuals. The tests were conducted on a revisited New England 39-Bus system with large scale photovoltaic (PV) power plant. The system was affected with multiple system disturbances and severe data-injection attacks. The results show the effectiveness of the proposed MRF method against the mainstream and regression methods. The proposed scheme can accurately estimate the states and evaluate the contaminated measurements while improving the situation awareness of wide area monitoring systems (WAMS) operations in modern power grids

A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment

A smart grid (SG), considered as a future electricity grid, utilizes bidirectional electricity and information flow to establish automated and widely distributed power generation. The SG provides a delivery network that has distributed energy sources, real-time asset monitoring, increased power quality, increased stability and reliability, and two-way information sharing. Furthermore, SG provides many advantages, such as demand response, distribution automation, optimized use of electricity, economical energy, real-time grid status monitoring, voltage regulation or VAR control, and electricity storage. In this survey, we explore the literature on smart Grid enabling technologies until 2022. We dig out four major systems: (1) the smart grid’s prominent features and challenges; (2) the smart grid standard system and legislations; (3) smart grid energy subsystem; and (4) the smart grid management system and protection system for new researchers for their future projects. The research challenges and future recommendations are also presented in the conclusion section to explore the new paradigm.

Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration

Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate renewable curtailment. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that assures limited constraint violations for any uncertainty distribution within an ambiguity set. Here, the second-stage recourse variables are represented as linear functions of uncertainty, yielding an equivalent reformulation involving linear constraints only. We utilize moment-based (mean-mean absolute deviation) and distance-based ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> -Wasserstein distance) ambiguity sets that lead to scalable mixed-integer linear program (MILP) formulations. Numerical experiments on the IEEE 14-bus and 118-bus systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of guaranteed constraint violations and reduced renewable curtailment. In particular, the computational efficiency of the moment-based MILP approach, which is scenario-free with fixed problem dimensions, has been confirmed, making it suitable for real-time grid operations.

Intelligent retrieval method for power grid operation data based on improved SimHash and multi-attribute decision making

IN the trend of energy revolution, power data becomes one of the key elements of the power grid. And an advance power system with "electric power + computing power" as the core has become an inevitable choice. However, the traditional search approach based on directory query is commonly used for power grid operation data in domestic and international. The approach fails to effectively meet the user's need for fast, accurate and personalized retrieval of useful information from the vast amount of power grid data. It seriously affects the real-time availability of data and the efficiency of business-critical analytical decisions. For this reason, an intelligent retrieval approach for power grid operation data based on improved SimHash and multi-attribute decision making is proposed in this paper. This method elaborates the properties of SimHash and multi-attribute decision making algorithms. And an intelligent parallel retrieval algorithm MR-ST based on MapReduce model is designed. Finally, real time grid operation data from multiple sources are analyzed on the cloud platform for example. The experimental results show the effectiveness and precision of the method. Compared with traditional methods, the search accuracy rate, search completion rate and search time are significantly improved. Experiments show that the method can be applied to intelligent retrieval of power grid operation data.

Frequency Regulation of an Islanded Microgrid Using Hydrogen Energy Storage Systems: A Data-Driven Control Approach

Hydrogen energy storage (HES) systems have recently received attention due to their potential to support real-time power balancing in a power grid. This paper proposes a data-driven model predictive control (MPC) strategy for HES systems in coordination with distributed generators (DGs) in an islanded microgrid (MG). In the proposed strategy, a data-driven model of the HES system is developed to reflect interactive operations of an electrolyzer, hydrogen tank, and fuel cell, and hence the optimal power sharing with DGs is achieved to support real-time grid frequency regulation (FR). The MG-level controller cooperates with a device-level controller of the HES system that overrides the FR support based on the level of hydrogen. Small-signal analysis is used to evaluate the contribution of FR support. Simulation case studies are also carried out to verify the accuracy of the data-driven model and the proposed strategy is effective for improving the real-time MG frequency regulation compared with the conventional PI-based strategy.

Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization

In the current era of e-mobility and for the planning of sustainable grid infrastructures, developing new efficient tools for real-time grid performance monitoring is essential. Thus, this paper presents the prediction of the voltage stability margin (VSM) of power systems by the critical boundary index (CBI) approach using the machine learning technique. Prediction models are based on an adaptive neuro-fuzzy inference system (ANFIS) and its enhanced model with particle swarm optimization (PSO). Standalone ANFIS and PSO-ANFIS models are implemented using the fuzzy ‘c-means’ clustering method (FCM) to predict the expected values of CBI as a veritable tool for measuring the VSM of power systems under different loading conditions. Six vital power system parameters, including the transmission line and bus parameters, the power injection, and the system voltage derived from load flow analysis, are used as the ANFIS model implementation input. The performances of the two ANFIS models on the standard IEEE 30-bus and the Nigerian 28-bus systems are evaluated using error and regression analysis metrics. The performance metrics are the root mean square error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (R) analyses. For the IEEE 30-bus system, RMSE is estimated to be 0.5833 for standalone ANFIS and 0.1795 for PSO-ANFIS; MAPE is estimated to be 13.6002% for ANFIS and 5.5876% for PSO-ANFIS; and R is estimated to be 0.9518 and 0.9829 for ANFIS and PSO-ANFIS, respectively. For the NIGERIAN 28-bus system, the RMSE values for ANFIS and PSO-ANFIS are 5.5024 and 2.3247, respectively; MAPE is 19.9504% and 8.1705% for both ANFIS and PSO-ANFIS variants, respectively, and the R is estimated to be 0.9277 for ANFIS and 0.9519 for ANFIS-PSO, respectively. Thus, the PSO-ANFIS model shows a superior performance for both test cases, as indicated by the percentage reduction in prediction error, although at the cost of a higher simulation time.

Real-Time Grid and DER Co-Simulation Platform for Testing Large-Scale DER Coordination Schemes

Distributed energy resources (DERs), such as responsive loads and energy storage systems, can help grid operators to more effectively balance supply and demand and manage network constraints. However, consumer acceptance of load coordination schemes depends on ensuring customer quality of service (QoS), thus requiring the careful management of device-level constraints. Simultaneously managing both device-level and system-level objectives is a difficult problem. Most potential solutions to the DER coordination problem are in some way distributed, with decision logic running both at the device (such as a thermostat) and the coordinator, with potentially complex communications between devices and one or more coordinators. It is difficult to effectively test distributed DER control schemes, particularly when there are thousands of devices with both device-level and grid-level constraints and asynchronous communications. This paper addresses this challenge by presenting a novel real-time grid-and-DER cyber-physical co-simulation platform for testing advanced DER coordination schemes and characterizing the behaviors of a DER fleet. The co-simulation platform is particularly suitable for: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> ) testing the real-time performance of a large fleet of DERs delivering advanced grid services; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula> ) characterizing the distribution of states for a large fleet of managed DERs; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula> ) incorporating practical limitations of DERs and communications and analyzing the effects on fleet-wide performance. To illustrate these benefits, we demonstrate how the platform can be used to test a fleet with thousands of DERs coordinated using Packetized Energy Management (PEM) and interacting with a realistic transmission system model.

Artificial Neural Networks – Based Method for Enhancing State Estimation of Grids with High Penetration of Renewables

This paper addresses state estimation as one of the most essential mechanisms in real-time operation and control of modern power systems, and proposes a novel solution to the issue of poor network observability, commonly faced in distribution system state estimation (DSSE) characterized by an ever-increasing penetration of renewable generation. The ongoing transformation from conventional passive, onedirectional power systems to active smart grids necessitates more accurate and reliable system state estimation to achieve optimal system performance. Real-time grid monitoring and control has been a routine task in transmission networks, but distribution grids cannot successfully utilize these capabilities due to different topologies, specific electrical characteristics, the low amount of available real-time measurements, as well as substantial communication effort needed to handle the data. Furthermore, with the advent of distributed generation, new types of loads and the vast surge of prosumers, a substantial amount of data is required to maintain system stability and controllability. For these reasons, reliable state estimation requires a high-quality creation process of pseudo-measurement, in addition to an efficient algorithm and an extremely accurate estimator. Thus, this paper proposes a novel framework of dynamic estimation methodology that includes the use of Artificial Neural Networks (ANN) in the pseudo-measurements generation process, utilizes Iteratively Reweighted Least Squares (IRWLS) algorithm and Schweppe-Huber Generalized Maximum Likelihood (SHGM) estimator. The efficiency and accuracy of the proposed methodology were assessed and verified on a benchmark network model.

Adaptive real-time grid operation via Online Feedback Optimization with sensitivity estimation

In this paper we propose an approach based on an Online Feedback Optimization (OFO) controller with grid input–output sensitivity estimation for real-time grid operation, e.g., at subsecond time scales. The OFO controller uses grid measurements as feedback to update the value of the controllable elements in the grid, and track the solution of a time-varying AC Optimal Power Flow (AC-OPF). Instead of relying on a full grid model, e.g., grid admittance matrix, OFO only requires the steady-state sensitivity relating a change in the controllable inputs, e.g., power injections set-points, to a change in the measured outputs, e.g., voltage magnitudes. Since an inaccurate sensitivity may lead to a model-mismatch and jeopardize the performance, we propose a recursive least-squares estimation that enables OFO to learn the sensitivity from measurements during real-time operation, turning OFO into a model-free approach. We analytically certify the convergence of the proposed OFO with sensitivity estimation, and validate its performance on a simulation using the IEEE 123-bus test feeder, and comparing it against a state-of-the-art OFO with constant sensitivity.

Cross-layer design for real-time grid operation: Estimation, optimization and power flow

In this paper, we propose a combined Online Feedback Optimization (OFO) and dynamic estimation approach for a real-time power grid operation under time-varying conditions. A dynamic estimation uses grid measurements to generate the information required by an OFO controller, that incrementally steers the controllable power injections set-points towards the solutions of a time-varying AC Optimal Power Flow (AC-OPF) problem. More concretely, we propose a quadratic programming-based OFO that guarantees satisfying the grid operational constraints, like admissible voltage limits. Within the estimation, we design an online power flow solver that efficiently computes power flow approximations in real time. Finally, we certify the stability and convergence of this combined approach under time-varying conditions, and we validate its effectiveness on a simulation with a test feeder and high resolution consumption data.

Security Assessment of Coal Mine Power Grid Voltage Based on an Improved AHP-FCE

The voltage level of a coal mine power grid directly affects the starting and normal operation of the electric motor, but the current grid monitoring system cannot use real-time grid structure parameters and power flow distribution data to conduct early warning research on the running status and voltage level of the power grid, so it cannot eliminate hidden danger in the operation of the power grid. Therefore, it is necessary to accurately and effectively evaluate the voltage safety level of the coal mine power grid. In this paper, an improved AHP-FCE method is proposed to evaluate the voltage safety level of a coal mine power grid. By replacing the comparison judgment matrix with the interval judgment matrix, the objectivity of the weight vector is improved while retaining expert subjectivity. According to the real-time power flow distribution data of the power grid monitoring system, the current voltage safety level of the power grid is evaluated. The results show that the improved AHP-FCE method can accurately evaluate the current grid voltage level, analyze the voltage development trend, give early warnings regarding coal mine grid voltage operation, and improve the monitoring function of the power monitoring system.

WAMS Based Real-Time Voltage Stability Monitoring for Various Load Models in the Presence of a DFIG Integrated Wind Farm

With rising global energy demand, transmission lines are operated at their limits, causing electrical grids to operate under extreme voltage instability conditions. To meet the increased load-demand and the forced tendency across the globe of tilting towards even more renewable generation, solar and wind farms are integrated with increased size and capacity. These uncontrolled natural power injection again may affect the system’s voltage stability. To avoid blackouts and to achieve the maximum voltage stability of power systems, operators need to do effective real-time grid monitoring and control at load terminals. Load models are important in the analysis of voltage stability, and accurate load models are useful in analysing the voltage stability conditions. Phasor-measurement-unit (PMU) based wide-area monitoring and smart-automation is an innovative technology for measuring load voltage magnitude, phase angle, and frequency variations in a DFIG integrated large wind power system. This research paper focuses on estimating the real-time voltage stability through the use of linear, nonlinear, and dynamic load models in presence of a DFIG based wind-farm in WSCC three-machine nine-bus power network using PMU data. This study is carried out using MATLAB-Simulink software.

Small-Signal Stability Improvement of Microgrid With Battery Energy Storage System Based on Real-Time Grid Impedance Measurement

Grid impedance has a significant impact on the small-signal stability and control of grid-connected power converters used for connecting multiple distributed energy resources and in various motor drive applications. To apply advanced control schemes, such as adaptive control, real-time grid impedance is a necessary parameter that ensures stability margins of the system during operation. For real-time grid-impedance measurement purpose, broadband frequency-response measurement techniques based on the pseudorandom binary sequence can be used. In this article, a computationally effective perturbation signal quadratic residue binary (QRB) sequence is proposed for real-time grid impedance measurement. Unlike the conventional perturbation signals, the QRB sequence has low crest factor and less perturbation duration resulting in minimal disturbance during impedance measurement. To extract the grid impedance in real-time, the QRB sequence is added to the reference <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-axis</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q-axis</i> currents of the battery energy storage system (BESS). Next, perturbed voltage and current signals are extracted in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abc</i> -frame and converted to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i> -frame, and grid impedance is assessed. Subsequently, the system stability is analyzed using the generalized Nyquist stability criterion. Later, an adaptive control algorithm is proposed and applied to the energy storage system (ESS) to enhance stability margins of the system based on the measured grid impedance. The effectiveness of the proposed perturbation sequence for impedance measurement and stability improvement algorithm are tested on a standard CIGRE microgrid system in the MATLAB/Simulink simulation environment. Experiments are conducted on a reduced scale 3 kVA laboratory testbed and results are presented to validate the efficacy of the proposed perturbation sequence and stability improvement algorithm.