Intermittent Earth Fault Detection In Distribution Network Based On The Voting Classification Technique

Intermittent earth faults in non-effectively distribution networks, especially with underground cabling, can compromise the quality of the electricity supply. This type of earth fault may be followed by permanent faults, which in turn puts the networks on the line. This phenomenon monitoring can help distribution system operators (DSOs) to plan maintenance to reduce system interruption and improve MV electricity delivery. Thus, this research will examine AI-driven approaches, which are suitable for complicated issues, to improve distribution power grid monitoring and maintenance. The research focuses on medium and low-voltage grids and applies the voting classification technique (VC) to monitor and predict earth faults. Moreover, IEC 61850 Sampled Value communication protocol will be utilized at a practical level to establish a hierarchical infrastructure of data processing nodes. VC will process this raw data to determine the distribution network condition. In this endeavor, a new efficient way to monitor and maintain power networks will be examined. The suggested method will predict the existing and future status of the system, including upcoming breakdowns. At the top of the structure, aggregated information will be displayed to human grid operators to help them schedule maintenance or plan emergency actions. Real grid pilots and laboratory experiments in Finland will provide the required data to develop and train the suggested approach to predict intermittent earth faults.©2023 IET. This paper is a postprint of a paper submitted to and accepted for publication in 27th International Conference on Electricity Distribution (CIRED 2023) and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

An efficient framework for short-term electricity price forecasting in deregulated power market

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. BSA as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information (MI) and SVM. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.©2021 Institute of Electrical and Electronics Engineers. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/This research has been supported by University of Vaasa under Profi4/WP2 project with the financial support provided by the Academy of Finland.fi=vertaisarvioitu|en=peerReviewed

Direct Power Control based on Point of Common Coupling Voltage Modulation for Grid-Tied AC Microgrid PV Inverter

In this paper, a direct power control (DPC) approach is proposed for grid-tied AC MG’s photovoltaic (PV) voltage source inverter (VSI) to regulate directly active and reactive powers by modulating microgrid’s (MG) point of common coupling (PCC) voltage. The proposed PCC voltage modulated (PVM) theory-based DPC method (PVMT-DPC) is composed of nonlinear PVM, nonlinear damping, conventional feedforward, and feedback PI controllers. For grid synchronization rather than employing phase-locked-loop (PLL) technology, in this study, direct power calculation of the PCC voltage and current is adopted. Subsequently, at PCC, the computed real and reactive powers are compared with reference powers in order to generate the VSI’s control signals using sinusoidal pulse width modulation (SPWM). Because of the absence of the PLL and DPC method adoption, the suggested controller has a faster convergence rate compared to traditional VSI power controllers. Additionally, it displays nearly zero steady-state power oscillations, which assures that MG’s power quality is improved significantly. To validate the proposed PVMT-DPC method’s performance real-time simulations are conducted via a real-time digital simulator (RTDS) for a variety of cases. The results demonstrate that PV VSI using the suggested PVMT-DPC approach can track the reference power quicker (0.055 s) along with very low steady-state power oscillations, and lower total harmonic distortion (THD) of 1.697% at VSI output current.© The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/This research has been supported by the University of Vaasa under the Centralized Intelligent and Resilient Protection Schemes for Future Grids Applying 5G (CIRP-5G) research project funded by Business Finland with Grant No. 6937/31/2021. Some parts of this work were done in the SolarX research project funded by Business Finland with Grant No. 6844/31/2018.fi=vertaisarvioitu|en=peerReviewed

Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Fuzzy Logic-Based Direct Power Control Method for PV Inverter of Grid-Tied AC Microgrid without Phase-Locked Loop

A voltage source inverter (VSI) is the key component of grid-tied AC Microgrid (MG) which requires a fast response, and stable, robust controllers to ensure efficient operation. In this paper, a fuzzy logic controller (FLC)-based direct power control (DPC) method for photovoltaic (PV) VSI was proposed, which was modelled by modulating MG’s point of common coupling (PCC) voltage. This paper also introduces a modified grid synchronization method through the direct power calculation of PCC voltage and current, instead of using a conventional phase-locked loop (PLL) system. FLC is used to minimize the errors between the calculated and reference powers to generate the required control signals for the VSI through sinusoidal pulse width modulation (SPWM). The proposed FLC-based DPC (FLDPC) method has shown better tracking performance with less computational time, compared with the conventional MG power control methods, due to the elimination of PLL and the use of a single power control loop. In addition, due to the use of FLC, the proposed FLDPC exhibited negligible steady-state oscillations in the output power of MG’s PV-VSI. The proposed FLDPC method performance was validated by conducting real-time simulations through real time digital simulator (RTDS). The results have demonstrated that the proposed FLDPC method has a better reference power tracking time of 0.03 s along with reduction in power ripples and less current total harmonic distortion (THD) of 1.59%.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach

In this paper, a hybrid electricity price forecasting method which is composed of two-stage feature selection method and optimized adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine is proposed to accurately forecast electricity price. A multi-objective feature selection approach comprises of multi-objective binary-valued backtracking search algorithm (MOBBSA) as an efficient evolutionary search algorithm and ANFIS method is developed in this paper to extract the most influential subsets of input variables with maximum relevancy and minimum redundancy. Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. Real-world electricity demand and price dataset from Ontario power market; which is reported as among the most volatile market worldwide, has been used as case study to validate the performance of the proposed approach. From the simulation results, it has been seen that the proposed hybrid forecasting method was effective in accurately forecast the Ontario electricity price. In addition, to prove the superiority of the proposed hybrid forecasting method the simulation results obtained using ANN and ANFIS models optimized by other well-known optimization methods have been compared with that of proposed method. © 2019 IEEE

Hybrid ANN and Artificial Cooperative Search Algorithm to Forecast Short-Term Electricity Price in De-Regulated Electricity Market

Smart grid has evolved into a viable platform for participants of electricity market to effectively regulate their bidding strategies based on demand-side management (DSM) models ascribed to its immense technological advancements in recent years. Reliability of system operation as well as capital cost investments can improve greatly with responsiveness of market participants. In this regard, efficient design, implementation, evaluation of numerous demand response measures and development of robust short-term price forecasting in the day-ahead transactions are of the utmost importance. Accuracy and efficiency of the day-ahead price forecasting process are complex challenges in deregulated electricity market. The unstable nature of electricity price compared to load series causes lower accuracy. Therefore, this research proposes a hybrid method for electricity price forecasting via artificial neural network (ANN) and artificial cooperative search algorithm (ACS). In parallel, a feature selection technique based on the combination of mutual information (MI) and neural network (NN) is developed in this study to select the input variables subsets, which have substantial impact on forecasting of electricity price. Actual data sets are collected from Ontario electricity market of the year 2017 for the verification of simulation results. Finally, the simulation results validated the premise of the proposed hybrid method through enhanced accuracy compared to the results acquired by implementing hybrid support vector machine (SVM) and hybrid ANN optimization methods. © 2013 IEEE