OPENMODS 2.0 “Instrument Jamming Meeting” report

Major achievements The feedback provided by potential users on their needs was very much appreciated. They underlined the importance of having: ● an easy to deploy instrument (i.e.: from small fishing boats); ● multi-parameter sensors in ONE device; ● less maintenance effort and prioritized the variables to measure. Although, there are technical limitations and different solutions and there is no one tool that can do everything, which is low cost, has high resolution and low maintenance, the outcomes of the platforms/sensors/communications working group meet the main requirements that emerged. Priority was given to: ● a platform that will operate in drifter mode which is extremely easy to deploy and perfect for studies associated with search and rescue operations (another need that has emerged). It also constantly guarantees the knowledge of the instrument position. The platform can be easily converted into the moored mode. ● temperature and pressure sensors. The sensors will be low -cost with the idea to replace them rather than calibrate them; ● LoRaWAN communications preferably with Bluetooth integration for the in-situ download of the data

A survey on control issues in renewable energy integration and microgrid

Abstract This paper describes the usefulness of renewable energy throughout the world to generate power. Renewable energy adds a remarkable scope in power system. Renewable energy sources act as the prime mover of a microgrid. The Microgrid is a small network of power system with distributed generation (DG) units connected in parallel. The integration challenges of renewable energy sources and the control of microgrid are described in this paper. The varied nature of DG system produces voltage and frequency deviation. The unknown nature of the load produces un-modeled dynamics. This un-modeled dynamic introduces measurable effects on the performance of the microgrid. This paper investigates the performance of the microgrid against different scenarios. The voltage of the microgrid is controlled by using different controllers and their results are also investigated. The performance of controllers is investigated using MATLAB/Simulink SimPowerSystems

Voltage and current control augmentation of islanded microgrid using multifunction model reference modified adaptive PID controller

Future grids will consist of many small and large microgrids that will require complex control of grid interaction as well as islanding operation. This paper addresses the control of islanded microgrids under dynamic system conditions. In this paper, a robust model reference modified adaptive proportional integral derivative (PID) controller is designed for voltage and current control augmentation of islanded microgrid against various load dynamics. The variation of load dynamics may cause the unsafe operation of the microgrid. The objective of the designed controller is to confirm the safe operation against voltage and current control under different load dynamics. The controller is designed based on selecting the desired reference model and controlling parameters. The effectiveness of the proposed controller is evaluated against the presence of load dynamics, harmonic sources, asynchronous machines, nonlinear and unknown loads. The results show that the proposed controller is capable of providing high tracking performance and safe operation for both the single and three phase islanded microgrid system

Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model

Robust Extended H∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid

This paper presents the design of an extended parameterisations of H∞ controller for off grid operation of a microgrid. The microgrid consists of distributed generation units, filters and local loads. The filters are used to achieve accurate sinusoidal output voltage. However, loads which are connected to the microgrid are parametrically uncertain. Hence, it undergoes with unknown loads uncertainties. These unknown loads may create unknown loads harmonics, non-linearities which may reduce the voltage and current profile of the microgrid. As a result, the sudden rise and fall of voltage current profile damages the domestic and commercial loads. The proposed controller provides robust stability against various unknown loads and uncertainties. The design of the controller is presented using linear matrix inequality approach and satisfies the Lyapunov stability criterion. Moreover, it provides lower closed-loop H∞ norm and has better tracking accuracy than other. For justification, several load conditions have been tested in MATLAB/SimPowerSystem Toolbox to ensure the robust stability of the proposed controller. All the results presented in the paper indicate high performance of the controller

Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model

Power transformer health condition evaluation: A deep generative model aided intelligent framework

This paper presents a deep generative model-aided intelligent framework for effective health condition evaluation of power grid transformers. The health assessment of a power transformer is required to guarantee the stable and sustainable operation of the grid and to precisely convert the electrical energy. A power transformer must undergo a series of tests to determine its state of health and identify its health index. In this paper, we develop a novel approach to identify and classify the health condition of power transformers using a machine learning approach. The proposed framework is structured by using a multi-layer perception generative model with a logistic regression classifier. The developed model uses the twelve input layers which enables the model to effectively compressed the dataset and eight categories in the output classification layers. The effectiveness of the proposed model is examined on the real-world testing data set of 31 categories of six hundred and eight transformers. The obtained performance using the proposed framework confirms its efficacy in precisely evaluating the transformer's health condition. The obtained results have also been compared with the existing machine-learning models. The comparisons show that the proposed model outperforms the state-of-the-art models by achieving 99% of accuracy. 2023 Elsevier B.V.Scopu

Uncertainty awareness in transmission line fault analysis: A deep learning based approach

With the expansion of the modern power system, it is of increasing significance to analyze the faults in the transmission lines. As the transmission line is the most exposed element of a power system, it is prone to different types of environmental as well as measurement uncertainties. This uncertainties influence the sampled signals and negatively affects the fault detection and classification performance. Therefore, an unsupervised deep learning framework named deep belief network is presented in this paper for fault detection and classification of power transmission lines. The proposed framework learns the beneficial feature information from the uncertainty affected signals with a unique two stage learning strategy. This strategy enables the proposed framework to extract lower level fault-oriented information which may remain unobserved for other alternative approaches. The efficacy of the proposed framework has been examined on the IEEE-39 bus benchmark topology. The in-depth accuracy assessment with different accuracy metrics along with exclusive case studies such as the influence of noise, measurement error as well as line and source parameter variations will be conducted in this paper to justify the real-world applicability of the proposed framework. Furthermore, the relative performance assessment with the cutting-edge rival techniques is also presented in this paper to verify if the proposed framework attains a state-of-the-art classification performance or not. 2022 Elsevier B.V.The publication of this article was funded by Qatar National Library .Scopu

Ancillary Voltage Control Design for Adaptive Tracking Performance of Microgrid Coupled with Industrial Loads

Although the utilizing of renewable energy sources (RESs) in microgrid (MG) offers a recognized solution to meet the increasing demand, it's performance depend on various meteorological factors of RESs. Again, the functioning of MGs is often affected with certain industrial load dynamics which allowing them to alter the operating region and tracking function of the MGs. The above-mentioned challenges motivate us to design the ancillary voltage control design for enabling the MGs to provide adaptive transient and tracking voltage responses over the changes of various factors like weather, consumer demand, and industrial loads. Firstly, we design an intelligent adaptive control (IAC) framework made by merging with proportional-integral (PI) regulator and artificial neural network (ANN) to sustain the regulated common bus voltage over the mentioned changes. The regulated bus voltage is forwarded to operate the industrial loads via the regulation of inverter-based secondary network (SN). A study on the variation of weather condition and consumer demand is done to show the efficacy of the IAC framework. Secondly, we propose a novel fixed control structure named model reference modified fractional-order PID (MR-F0PID) regulator to maintain the high tracking response of the MG via the control of inverter associated with the SNs. The tracking competency of this fixed control framework is analyzed over the running of a few industrial loads dynamics associated with single-phase inverter based SN and results are compared with the other related existing controllers. Moreover, a mathematical analysis for mapping the stable region is completed here to track down the closed-loop stability area. As a further study, the three-phase inverter based SN associated with several three-phase industrial load is also considered with the same DC bus and analyzed to observe the competency of the proposed fixed MR-FOPID control framework.</p