Local Voltage Measurements Research Articles

Investigation of current density and temperature distribution characteristics under transient conditions based on a 108 cm2 segmented proton exchange membrane fuel cell

Visualizing the electrochemical and temperature distribution characteristics inside proton exchange membrane fuel cell (PEMFC) should greatly contribute to the product development and performance optimization. In the study, a customized 27-segment 108 cm2 fuel cell was fabricated based on the printed circuit board methodology to achieve the local current density and local voltage measurement. In addition, 27 thermocouples were embedded in the segmented cathode graphite gas flow plate for local temperature measurement. Detailed information of fuel cell core components, multi-channel data acquisition system, external temperature control system, and uncertainty analysis was presented. According to the test results of 4000 s current rising conditions and 2400 s current decreasing conditions, the measurement deviation of total current between data acquisition system and TOYO test station is 2.4%–3.2%. Meanwhile, the deviation of output voltage ranges from 0.1% to 2.6%. To quantify the degree of distribution non-uniformity, the range and standard deviation of local parameter values as well as contour map are demonstrated. The non-uniformity of local current density and local temperature becomes more significant under higher operating current conditions. Furthermore, the degree of parameter non-uniformity is more noticeable under current rising conditions compared with current decreasing conditions. The transient increase of operating current leads to the sudden overshoot phenomena of local current range, and the change of local current density among all segments is instantaneously finished. However, the range of current demonstrates relatively smooth transition trends when reactant flow rate rises. Higher temperature area appears in the middle of membrane electrode assembly, especially in the position with higher local current density. The parameter distribution and transition characteristics could provide helpful guidance for fuel cell optimization and diagnosis.

Non-Invasive Measurement of Impedance Spectra Distribution in Polymer Electrolyte Fuel Cells

Localized measurement of the impedance distribution in an operating polymer electrolyte fuel cell stack is of great importance in providing information on its state of health and performance. However, the current methods’ invasiveness is a brake to a more widespread utilization. This work presents a non-invasive way to measure and interpret local impedance spectra. It is based on local AC current supply and local voltage measurements performed all around the cell via conductive pins contacting the outer surface of the flow fields. The different local spectra are measured on a single cell within a 200 cm2 six-cell stack under different operation conditions and compared to a reference case with homogeneous current density. A simple equivalent electrical circuit is used to fit the impedance spectra and to extract membrane and charge transfer resistance. The variations of these two values are analyzed at five different positions along the channel for different cathode stoichiometries and gas flow configurations.

Measurement and simulation of voltage differences under ribs and channels and temperature variations within channels in PEMEC

The internal voltage inhomogeneity in a proton exchange membrane electrolyzer cell (PEMEC) is experimentally investigated by using customized gaskets. Three miniature thermocouples are strategically placed inside the channels, specifically in the inlet region, middle region, and outlet region, to monitor temperature variations along the channels. Local voltage measurements are conducted in 13 regions corresponding to the flow channels and 12 regions corresponding to the ribs, under various operating conditions. Additionally, a three-dimensional simulation is performed to analyze the underlying causes of non-uniform voltage distribution and temperature variation. The results reveal contrasting trends in temperature changes along the flow direction, attributable to the varying magnitudes of heat loss and electrochemical heat at different current densities. At low current densities, temperatures increase from the inlet to the outlet along the channels, while at high current densities, temperatures decrease in the same direction. Furthermore, the local voltages of the flow channels (Vchan) and the ribs (Vrib) exhibit different increases along the flow direction at various current densities with the Vchan consistently smaller than Vrib. The inhomogeneity of Vchan is found to be greater than that of Vrib due to bubble disturbances in the channels. The proposed measurement principle for local voltage distribution in PEMECs provides insights into the performance discrepancies between the channel and rib scales in the flow field, thereby enhancing our understanding of electrical property inhomogeneity within the cell.

AC/DC Hybrid Power System Damping Control Based on Estimated Model Predictive Control Considering the Real-Time LCC-HVDC Stability

High Voltage Direct Current (HVDC) can quickly vary its power to stabilize an AC power system when the latter is subject to a disturbance. Typically, linear control is used to design that control strategy. However, due to the uncertainties of the faults and the system model, it is necessary to estimate the linear model of the post-fault power system using real-time measurements. This leads to two main problems: Firstly, after the fault, the HVDC will quickly evolve to steady-state operating conditions and, therefore, result in an unobservable control matrix. Secondly, the HVDC power should not exceed its stable range, which is limited by the post-fault system. To address the above issues, the main novelties are as follows: First, this paper proposes a novel system identification method to handle that case when there is no control input excitation. Second, this paper presents an AC/DC hybrid power system control strategy that fully considers the real-time stability constraints of the HVDC. More specifically, this paper proposes to estimate the system matrix and the control matrix separately. Based on the linearized model of the AC/DC hybrid system, the control matrix is calculated using the sensitivities of the active powers of the generators with respect to those of the HVDC. Then, the system matrix is estimated in real-time using measures of the power angle and frequency. Finally, by processing the local voltage and current measurements, the associated Thevenin equivalent parameters on the system side of the HVDC connecting point are estimated in real-time and the upper bound of the DC power is determined. According to the linearized model and the HVDC power constraints, a control strategy is proposed using the model predictive control method. Simulation results reveal the effectiveness of the proposed strategy.

Cyberattack on Phase-Locked Loops in Inverter-Based Energy Resources

This article reveals power grid vulnerabilities by manipulating the phase-locked loop (PLL) in an inverter-based energy resource (IBER). An attacker can exploit the off-nominal frequency operation of a power grid coupled with stability and tracking properties of an IBER PLL to manipulate the inverter and grid operations during strong and weak grid conditions. Employing a modified IEEE-14 bus system, simulation studies show that PLL-based attacks can cause: (i) revenue losses to IBER operators in weak and strong grid conditions, (ii) voltage limit violations and voltage profile deterioration at IBER bus under weak grid condition, and (iii) system-wide power-angle instability under weak and strong grid conditions. To detect the PLL-based attack, we propose attack severity index (ASI) by generating a detection error signal from local grid voltage measurements and PLL estimation. The attack is mitigated by incorporating security as an integral part of the PLL design, yielding a security-aware PLL.

Noninvasive Local Impedance Determination in Polymer Electrolyte Fuel Cells Stacks

Water management in polymer electrolyte fuel cells (PEFC) needs to ensure a homogeneous membrane humidification without local dryout or flooding from inlet to outlet of cell with active areas of some hundreds of square centimeters. Measurement methods that are able to provide spatial resolved information on the current density, membrane conductivity or water content distributions of PEFCs are usually invasive and therefore difficult to use in actual applications. Based on the learnings of the development of electrical impedance tomography (EIT) for PEFCs [1], a new method is presented that is capable to determine 1D humidity profiles between the gas inlet and outlet in a 6-cell commercial stack [2,3]. Local AC current injections and voltage measurements are performed using electrodes positioned all around the fuel cell attached to the flow field plates (see Figure 1a). Each electrode combination characterizes a specific area of the cell as the current crosses the membrane electrode assembly (MEA) very locally.The presentation will discuss the calibration of the local impedance values, the sensitivity of the approach to relative humidity (rH) gradients in the cell (see Figure 1b) and present use cases of measurements during fuel cell operation. Finally, we will present results on multiply frequency AC stimulation that allows local electrochemical impedance measurements and thereby insights beyond membrane conductivity, i.e. into mass transport limitations due to water accumulations in the cell.

Abstract 17540: Functional Mitral Regurgitation is Associated With Decreased Papillary Muscle Viability Patients With Non-Ischemic Dilated Cardiomyopathy

Background: The underlying mechanisms of functional mitral regurgitation (FMR) in non-ischemic cardiomyopathy (NICM) are incompletely understood. Aims: We investigated the correlation between papillary muscle viability and FMR in NICM. Methods: We enrolled 66 patients with NICM (LVEF<40%, NHYA III) undergoing CD34 + cell transplantation. At baseline, patients underwent electroanatomical mapping; papillary muscle viability was quantified by local unipolar voltage (UV) measurement, and decreased viability was defined as UV<8.3 mV. All patients received granulocyte-colony stimulating factor; CD34 + cells were collected by apheresis and delivered by transendocardial injections. Echocardiography was performed at baseline and 6 months after cell therapy; severity of FMR was graded by multi-parametric approach. FMR improvement was defined as a reduction in FMR by ≥1 grade when compared to baseline. Results: At baseline, papillary muscle viability was decreased in 24 (36%) and preserved in 42 (64%) of patients. Moderate to severe (2-3+) FMR was more common in patients with decreased papillary muscle viability (6/24; 25%) than in patients with preserved viability (2/42; 5%, P=0.015). Transendocardial cell injections were performed in the papillary muscle area in 26 patients (Group A) and in other areas of the left ventricle in 40 patients (Group B). The groups did not differ in age (56±11 years in Group A vs. 54±10 years in Group B, P=0.32), sex (male: 96% vs. 85%, P=0.21), creatinine (0.98±0.27 mg/dL vs. 0.96±0.28 mg/dL, P=0.92), bilirubin (0.89±0.37 mg/dL vs. 0.92±0.35 mg/dL, P=0.45), LVEF (34±9% vs. 31±7%, P=0.12), NT-proBNP levels (1196±994 pg/mL vs. 1077±1176 pg/mL, P=0.68), the degree of FMR (1.28±0.78 vs. 1.02±0.38, P=0.20), or papillary muscle viability (9.9±3.0 mV vs. 10.3±3.2 mV, P=0.62). At 6 months after cell injection, FMR improved in 77% of patients in Group A vs. 13% in Group B (P=0.001). On multivariate analysis, cell injection in the papillary muscle area was an independent correlate of FMR improvement (P=0.01). Conclusions: FMR in patients with NICM is associated with decreased papillary muscle viability. Transendocardial injection of CD34 + cells in the papillary muscle area may improve FMR in this patient population.

Can Locational Disparity of Prosumer Energy Optimization Due to Inverter Rules Be Limited?

To mitigate issues related to the growth of variable smart loads and distributed generation, distribution system operators (DSO) now make it binding for prosumers with inverters to operate under pre-set rules. In particular, the maximum active and reactive power set points for prosumers are based on local voltage measurements to ensure that inverter output does not cause voltage violations. However, such actions, as observed in this work, restrict the range available for local energy management, with more adverse losses on arbitrage profits for prosumers located farther away from the substation. The goal of the paper is three-fold: (a) to develop an optimal local energy optimization algorithm for activation of load flexibility and inverter-interfaced solar PV and energy storage under time-varying electricity prices; (b) to quantify the locational impact on prosumer arbitrage gains due to inverter injection rules prevalent in different energy markets; (c) to propose a computationally efficient hybrid inverter control policy which provides voltage regulation while substantially reducing locational disparity. Using numerical simulations on three identical prosumers located at different parts of a radial feeder, we show that our control policy is able to minimize locational disparity in arbitrage gains between customers at the beginning and end of the feeder to 1.4%, while PV curtailment is reduced by 91.7% compared to the base case with restrictive volt-Var and volt-watt policy.

Communication-less adaptive directional overcurrent protection strategy considering islanded mode detection in active distribution networks

The integration of distributed energy resources (DERs) in electric distribution networks originates the Active Distribution Networks (ADNs). The presence of DERs introduces challenges to conventional protection schemes due to bidirectional power flows and variations in fault current magnitudes; furthermore, the connection and disconnection of DERs result in multiple ADN operating modes (OMs), deteriorating the effective operation of protective relays. Considering the above, this document presents an adaptive protection strategy to address the challenges related to the variable nature of the ADN operation. The main contribution of the proposed approach is accurate islanded mode detection without requiring communication links, enabling fault detection in all ADN operating modes. The protection strategy maintains directional overcurrent relays' coordination using local voltage and current measurements to continuously estimate the Thevenin equivalent, fault currents, and adaptive coefficients.Relay coordination is maintained through tests conducted on a modified IEEE 34-bus feeder, considering several OMs, fault types, and locations. A comparison between the proposed adaptive protection strategy and a conventional overcurrent scheme shows that relay operating time is better in 100% of the analysed cases for the former strategy. On the other hand, the maximum errors in the fault current estimation using the Thevenin equivalent parameters are between 0.05% to 2.6% for all of the analysed operating modes when compared with the true fault current magnitudes. Finally, the adequate results validate the potential of the proposed communication-less protection strategy for modern protective relaying applications.

A consensus-based control for precise reactive power-sharing in AC microgrids

The objective of this work is to propose a control structure for managing the energy of distributed generators connected through a meshed AC network, which supplies a set of distributed loads. To avoid the need for algorithm switching in the event of faults (such as line losses or generator disconnections), all converters operate in grid forming mode. A control strategy based on nonlinear droop control ensures optimal network operation (equal sharing of active and reactive power) even in the presence of uncertainties. Only local voltage measurements are exchanged between neighboring nodes and used by a consensus algorithm to achieve the control objectives. The design of control parameters and hardware-in-the-loop (HIL) validations are presented in the article. The findings demonstrate that a low communication rate network is sufficient to achieve the desired objectives.

Automatic Self-Adaptive Local Voltage Control Under Limited Reactive Power

The increasing proliferation of distributed energy resources has posed new challenges to Volt/VAr control problems in distribution networks. To this end, this paper proposes an automatic self-adaptive local voltage control (ASALVC) by locally controlling VAr outputs of distributed energy resources. In this ASALVC strategy, each bus agent can locally and dynamically adjust its voltage droop function in accordance with time-varying system changes. The voltage droop function is associated with the bus-specific time-varying slope and intercept, which can be locally updated, merely based on local voltage measurements, without requiring communication. Stability, convergence, and optimality properties of this local voltage control are analytically established. In addition, the online implementation of ASALVC is further proposed to address the real-time system changes by adjusting VAr outputs of DERs online. Numerical test cases are performed to validate and demonstrate the effectiveness and superiority of ASALVC.

A continuous-time voltage control method based on hierarchical coordination for high PV-penetrated distribution networks

The photovoltaic (PV) integration brings both fast photovoltaic generation (PVG) variations and a large number of PV inverters to the distribution networks (DNs). The management of the PVG variation and the PV inverter is an urgent problem. The discrete-time model can’t describe PVG variations during the schedule interval and the regulation potential of the PV inverter is limited by a fixed dispatching value. This paper proposes a continuous-time voltage control method based on hierarchical coordination for high PV-penetrated DNs. In the central schedule, based on the Bernstein polynomial method, continuous-time models of PVGs are established and reference values of local inverter control curves are dispatched as continuous-time functions in a sub-hourly timescale. In the local control, reactive power and active power curtailment of PV inverters are set as the reference values, i.e., the corresponding values of the continuous-time functions, every 30 s to optimize the central dispatching objective under the voltage constrains, and deviate from the reference values according to both the local control curve and voltage measurement only when the real-time bus voltage violation occurs. In addition, the cooperation between inverter and mechanical equipment in the continuous-time framework is also considered, and the Bernstein polynomial method is developed to the continuous-time modeling of voltage control in DNs. The simulation shows that the proposed method has lower maximum voltage magnitude and lower energy loss compared with discrete-time central schedule method.

Forward engineering of local load control for primary frequency and voltage regulation

Recently, more frequency and voltage stability issues have been caused by the uncertain load demand and intermittent renewable energy generation in smart grids. This has put significant pressure on turbine governor action. One potential solution is load participation in primary control. In this study, we investigate a general class of local load control (LLC) to provide grid frequency and voltage support while minimizing the pre-defined disutility to utilities and clients. Our approach relies only on local frequency and voltage measurements. We prove that the network dynamics under optimality conditions solve the dual problem of the disutility function adaptively, providing a principled way to guide forward engineering in local load control design. Simulation-based comparative study of choosing different types of disutility functions is conducted on response performance concerning frequency/voltage nadir and steady-state error. Moreover, the effectiveness and rationality of the proposed approach are verified in MATLAB/SIMULINK under various scenarios, including load variations, renewable power output fluctuations, and different penetrations of renewable energy.

Coordinated Power Oscillation Damping From a VSC-HVDC Grid Integrated With Offshore Wind Farms: Using Capacitors Energy

This paper proposes a novel coordinated control strategy for a voltage source converter (VSC) based high-voltage direct current (HVDC) grid integrated offshore wind farms (OWFs) to damp the power system oscillations. A feature of this strategy is aiming to use the DC-link capacitor energy of offshore wind turbines (WTs) to reduce interactions between power oscillations and HVDC grid voltage when onshore grid-side VSCs (GSVSCs) modulate the active and reactive power injections. Unlike the previous communication-based method, the coordination from offshore WTs in this strategy depends on the local measurements of the HVDC grid voltage instead of the remote communication data from the onshore AC grid. A modified IEEE 39-bus power system with a 5-terminal VSC-HVDC grid connected to two OWFs has been developed to validate the effectiveness of this proposed strategy. Both the eigenvalue analysis and time-domain simulation results indicate that this strategy can significantly improve the power oscillation damping (POD). Comparative simulation studies also conclude that the proposed strategy has similar POD improvements to the previous communication-based method without the negative impact of communication delay from onshore to offshore.

Protection scheme for multi-terminal HVDC system with superconducting cables based on artificial intelligence algorithms

This paper presents the development of a novel data-driven fault detection and classification scheme for DC faults in multi-terminal HVDC transmission system which incorporates superconducting cables and modular multi-level converters. As the deployment of superconducting cables for bulk power transmission from remote renewable generation is progressively increasing in the future energy grids, many fault-related challenges have been raised (i.e., fault detection, protection sensitivity/stability). In this context, the applications of Artificial Intelligence techniques have started to be considered as a powerful tool for the development of robust fault management solutions. The proposed artificial intelligence-based method utilizes local current and voltage measurements to detect and classify all types of faults on the DC cables and DC buses, without the requirement of measurements exchange among different DC substations. The performance of the proposed scheme has been assessed through detailed transient simulation analysis and the results confirmed its effectiveness against a wide range of fault conditions (i.e., various fault types, fault locations and fault resistances). Furthermore, the feasibility of the developed scheme for real-time implementation has been validated using real-time software in the loop testing. The results revealed that the proposed algorithm can correctly, and within a very short period of time (i.e. less than 2 ms) detect and classify the faults within the protected zone and concurrently remain stable during external faults. Additionally, the generalization capability of the algorithm has been verified against influencing factors such as the addition of noise, highlighting the robustness of the presented scheme.

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. We test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

Data-Mining Techniques Based Relaying Support for Symmetric-Monopolar-Multi-Terminal VSC-HVDC System

Considering the advantage of the ability of data-mining techniques (DMTs) to detect and classify patterns, this paper explores their applicability for the protection of voltage source converter-based high voltage direct current (VSC-HVDC) transmission systems. In spite of the location of fault occurring points such as external/internal, rectifier-substation/inverter-substation, and positive/negative pole of the DC line, the stated approach is capable of accurate fault detection, classification, and location. Initially, the local voltage and current measurements at one end of the HVDC system are used in this work to extract the feature vector. Once the feature vector is retrieved, the DMTs are trained and tested to identify the fault types (internal DC faults, external AC faults, and external DC faults) and fault location in the particular feeder. In the data-mining framework, several state-of-the-art machine learning (ML) models along with one advanced deep learning (DL) model are used for training and testing. The proposed VSC-HVDC relaying system is comprehensively tested on a symmetric-monopolar-multi-terminal VSC-HVDC system and presents heartening results in diverse operating conditions. The results show that the studied deep belief network (DBN) based DL model performs better compared with other ML models in both fault classification and location. The accuracy of fault classification of the DBN is found to be 98.9% in the noiseless condition and 91.8% in the 20 dB noisy condition. Similarly, the DBN-based DMT is found to be effective in fault locations in the HVDC system with a smaller percentage of errors as MSE: 2.116, RMSE: 1.4531, and MAPE: 2.7047. This approach can be used as an effective low-cost relaying support tool for the VSC-HVDC system, as it does not necessitate a communication channel.

Master-slave strategy based in artificial intelligence for the fault section estimation in active distribution networks and microgrids

Fault location plays an essential role in the integration of self-healing functionalities in active distribution networks and microgrids. However, the fault location methods formulation presents great challenges for these types of networks because the operating changes that occur them, such as changes in topology, DER connection/disconnection and microgrids operating modes. Several fault location solutions have been proposed; nevertheless, these are strongly dependent on robust communication systems. This paper presents an artificial intelligence-based master–slave strategy for the estimation of the fault section in active distribution networks and microgrids using dispersed measurements. The strategy is composed by two stages. The master stage uses a genetic algorithm that determines the location and number of devices which maximize the faulted location e performance. The slave stage uses artificial neural networks to predict the fault section by using local voltage and current measurements trough an intelligent electronic device (IED). This approach is useful because it neglects the need of a robust communication systems and synchronization process between measurements. Here, each IED estimates the faulted section and then sends it through the single communication system to the distribution system operator control center. The presented method is validated on the modified IEEE 34-nodes test feeder where the accuracy of the strategy was 95%. The results obtained and its easy implementation indicate potential for real-life applications.

Harmonic Signature-Based One-Class Classifier for Islanding Detection in Microgrids

This article presents a new passive islanding detection technique in MGs that uses locally measured voltage signals at the PoC of DERs. The proposed method distinguishes islanding events from normal/non-islanding conditions by utilizing superimposed harmonic spectra extracted through a full-cycle discrete Fourier transform. Our solution utilizes a machine-learning-based one-class classifier to define and adjust thresholds for full harmonic spectra. Unlike other methods, our approach does not require data synchronization or communication infrastructure, nor does it suffer from common errors that often arise in current transformers. Moreover, our design is compatible with distributed and decentralized control strategies, as it relies solely on local voltage measurements at the PoC. Another advantage of this method is its low sampling frequency requirement, in the range of 1 kHz, making it cost-effective and implementable in most existing systems. In a comprehensive evaluation of a typical MG test system that included synchronous and inverter-based DERs, the proposed scheme demonstrated exceptional performance. Specifically, the scheme was able to detect 99.06% of different islanding events within the training range, with a detection time of just 10 to 21 ms. Additionally, the scheme remained 100% stable during various normal conditions, short-circuit faults, load changes, voltage changes, capacitor switching, and frequency changes.

An Adaptive Control of Remote Hybrid Microgrid based on the CMPN Algorithm

• The paper proposes a decentralized droop-based control technique power flow through the ILC, meanwhile, to keep the dc microgrid voltage and the ac microgrid frequency within their acceptable ranges. • To overcome the continuous operation of ILC, The modified droop control based CMPN algorithm is used to determine the necessity of operation of hybrid microgrid by estimating the status of the ac and dc microgrids by comparing local measurements with their critical values at both sides. • An adaptive filtering algorithm based on continuous mixed P-norm (CMPN) algorithm is utilized for the proposed autonomous control strategy to insure adaptive tuning of the controller parameters. The variable step size feature of the CMPN algorithm results in fast response compared to other techniques such as robust mixed-norm and the least absolute deviation techniques. The robustness against impulsive noise while having fast convergence, compared to other algorithms, render the CMPN algorithm to be an effective technique for online adaptation of the PI controller used for the ILC, especially in an islanded archetype to maintain the stability of the network. • A comparison between the proposed strategy based on both CMPN algorithm and adaptive PI control under different loading conditions are implemented to validate the proposed methodology. This paper proposes a new adaptive communication-less control strategy to enhance the dynamic performance of the interlinking converter (ILC) of a remote hybrid microgrid. The proposed autonomous strategy is based on the continuous mixed P-norm (CMPN) algorithm to simultaneously maintain the frequency of ac-side and the voltage of dc-side of the ILC within their acceptable ranges under various conditions. The variable step nature of the CMPN algorithm and robustness ensure stable operation of the proposed strategy for the ILC. In addition, the power sharing between both sides of the ILC is achieved by a modified ac-dc droop control to avoid using any communication systems between different distributed generators (DG). Moreover, a simple algorithm is used to determine the desired power flow direction based on local measurements of dc voltage and frequency. The proposed CMPN algorithm provides an online adaption of the controller's parameters to avoid the need for fine tuning or optimization techniques under various operating conditions. The dynamic performance of the proposed autonomous control strategy is evaluated using the PSCAD/EMTDC simulation package under different operating modes. The results demonstrate the superior dynamic performance of the proposed strategy based on the CMPN algorithm compared to the adaptive PI (API) controller in terms of fast response and tight regulation of power flow, voltage and frequency under different conditions. Compared to API, it has been found that the proposed controller results in more improvement of the dc-side voltage within the operating frequency range of the ac-side of the ILC.