OpenEdgePMU: An Open PMU Architecture with Edge Processing for Future Resilient Smart Grids

Livanos, Nikolaos-Antonios I.; Hammal, Sami; Γιαμαρέλος, Νικόλαος; Alifragkis, Vagelis; Psomopoulos, Constantinos S.; Zois, Elias N.

doi:10.3390/en16062756

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…Nonlinear approaches like RQP [45,51] are reported in [52] for power system and analysis applications. Also, OpenEdgePMU architecture is presented in [53]. Other techniques, such as an optimized extreme learning machine-based procedure and the utilization of synchrophasors to guarantee real-time power transient stability prediction, are proposed in [54].…”

Section: Introductionmentioning

confidence: 99%

The Branch-and-Bound Algorithm in Optimizing Mathematical Programming Models to Achieve Power Grid Observability

Theodorakatos,

Babu,

Moschoudis

2023

Axioms

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Phasor Measurement Units (PMUs) are the backbone of smart grids that are able to measure power system observability in real-time. The deployment of synchronized sensors in power networks opens up the advantage of real-time monitoring of the network state. An optimal number of PMUs must be installed to ensure system observability. For that reason, an objective function is minimized, reflecting the cost of PMU installation around the power grid. As a result, a minimization model is declared where the objective function is defined over an adequate number of constraints on a binary decision variable domain. To achieve maximum network observability, there is a need to find the best number of PMUs and put them in appropriate locations around the power grid. Hence, maximization models are declared in a decision-making way to obtain optimality satisfying a guaranteed stopping and optimality criteria. The best performance metrics are achieved using binary integer, semi-definite, and binary polynomial models to encounter the optimal number of PMUs with suitable PMU positioning sites. All optimization models are implemented with powerful optimization solvers in MATLAB to obtain the global solution point.

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Section: Introductionmentioning

confidence: 99%

The Branch-and-Bound Algorithm in Optimizing Mathematical Programming Models to Achieve Power Grid Observability

Theodorakatos,

Babu,

Moschoudis

2023

Axioms

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“…Subsequently, when an unknown data point x * appears, we can use this function in order to calculate/predict the respective output y * [19]. Equation (1) describes the relation between the input and output variables.…”

Section: Linear Regressionmentioning

confidence: 99%

“…The modernization of the communication infrastructure of the electrical grid, featuring smart sensors, IoT, and edge computing [ 1 ], as well as the deregulation of the electric power markets, has enabled the proliferation of distributed generation, mainly from renewable energy sources (RES) [ 2 ]. This new paradigm, while actualizing the much sought-after decarbonization of energy generation, has jeopardized the stability of the distribution network due to the intermittency of the aforementioned resources, mainly in isolated power grids, such as non-interconnected islands.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

Γιαμαρέλος

Papadimitrakis

Stogiannos

et al. 2023

Sensors

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The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy.

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“…In this context, the measurement infrastructure is expected to become the backbone of many monitoring and control applications [5], as state estimation [6], fault location [7], loss-of-mains detection [8], and under-frequency load shedding [9]. To this end, Phasor Measurement Units (PMUs) represent a promising solution due to their measurement accuracy and time synchronization [10,11]. In compliance with the recent IEC Std 60255-118-1 (briefly, IEC Std) [12], PMUs measure the phasor, frequency, and rate of change of frequency (ROCOF) of the fundamental component with reporting rates in the order of tens of frames per second.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation

Frigo,

Grasso-Toro

2023

Applied Sciences

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The ever-increasing penetration of renewable energy sources and distributed generation needs developing and deploying more sophisticated and precise control techniques. The paradigm shift from high-rotational inertia towards inverter-connected facilities has made modern power systems subject to strongly non-stationary operating conditions. In this context, phasor measurement units (PMUs) represent a promising solution due to their accuracy and time synchronization. The current paper further develops the concepts recently published proposing performance assessment of PMUs relying on metrics specifically defined to quantify the estimation accuracy, reporting latency, and response time. Our focus is on determining the confidence interval associated with these metrics and thus derive a robust approach for their application in measurement-based network controlling efforts. The configuration of the used simulation model is detailed, and the state estimation results as a function of the selected measurement weighing criteria are presented, concluding as a possible application of the reliability metrics in Weighted Least Squares and Discrete Kalman Filter state estimators, highlighting the performance enhancement as well as the theoretical limits of the proposed approach.

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OpenEdgePMU: An Open PMU Architecture with Edge Processing for Future Resilient Smart Grids

Cited by 5 publications

References 53 publications

The Branch-and-Bound Algorithm in Optimizing Mathematical Programming Models to Achieve Power Grid Observability

The Branch-and-Bound Algorithm in Optimizing Mathematical Programming Models to Achieve Power Grid Observability

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation

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