Reliable Operation Of Power System Research Articles

Short-Term Electrical Load Forecasting With Multidimensional Feature Extraction

Accurate short-term load forecasting (STLF) is required for reliable power system operations. Nevertheless, load forecasting remains a challenge owing to the high dimensionality and volatility of electrical load data as time series. In this study, a feature extraction framework for electrical load and other complementary data as a multivariate time series is proposed. The proposed framework consists of tagging and embedding processes that extract patterns from the multivariate time series as tags and capture their temporal and dimensional relations. In the embedding process, a network model that embeds the tags is deliberately designed with a convolutional layer in a multi-output structure based on mathematical analysis. Furthermore, a deep learning-based STLF model is constructed with the proposed feature extraction framework. The performance of the proposed STLF model for day-ahead load forecasting is evaluated on a publicly available set of real electricity demand data. The experimental results verify that the proposed approach reduces the root mean squared error by 5% to 12%. This improvement in load forecasts can benefit power grid operations as it provides more accurate expectations on the behaviors of the power grid in short term, which can be utilized in power grid applications, such as power dispatch and scheduling.

Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

Aiming at the problems of economy, security, and reliability of power system operation, the multiobjective gap evolutionary algorithm is introduced into power-sensitive early warning data conversion. A power-sensitive early warning data conversion (PSEWDC) method based on multiobjective gap evolutionary algorithm (MOGEA) is proposed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of defects and problems, the method quickly locates the problems and analyzes the causes, replaces and rearranges the original data by using the substitution data method to eliminate the nonlinear autocorrelation, and then, according to the convergence of the long-range correlation index, identifies the abnormal values that have no impact on the overall fluctuation of the original sequence, judges that the current sequence reaches the critical point of extreme events, and provides a reference threshold for real-time risk early warning. Finally, the example analysis shows that this method can finally determine the risk early warning threshold of power peak and valley load by analyzing the convergence of long-range correlation index, so as to avoid the blindness of subjective experience, provide a theoretical basis for power load risk early warning, effectively solve the problems of subjectivity, lack of dynamics, and lack of theoretical basis for setting risk threshold in traditional power risk early warning research, and have an intelligent insight into the security situation of power big data.

Location and corrosion detection of tower grounding conductors based on electromagnetic measurement

Tower grounding conductors are buried underground and serve as the grounded discharge channel for the fault or lightning current of a transmission power system. The performance of the grounding conductor is crucial for the safe and reliable operation of transmission power systems. This study proposes a steel corrosion detection and location method for tower grounding conductors using electromagnetic measurements. To measure the surface alternating current magnetic field from the tower grounding conductor, a three-axis magnetic sensor array was constructed using six anisotropic magnetoresistive (AMR) magnetic sensors with a small size (mm3). Through simulation analysis, the structural size of the magnetic sensor array was determined, and the feasibility of the detection principle was analyzed. The experimental results showed that the detection accuracy of the grounding conductor position can reach ± 0.1 m. The maximum deviation of the depth detection was 0.0964 m for the square-type grounding conductor. Furthermore, the simulation and experiment demonstrate that the value of fuzzy closeness decreases gradually with an increase in the degree of corrosion of the grounding conductor, proving that the degree of corrosion of the grounding conductor can be evaluated according to the value of fuzzy closeness. The proposed method has significant potential for engineering applications in the detection and maintenance of tower grounding conductors.

Development of Medium-Term Water Inflow Forecasting Models for Planning Electricity Generation in Isolated Power Systems

Reliable operation of power systems with a significant share of hydropower plants (HPPs) in the energy mix depends in many respects on how accurately the water inflow is forecasted. Taken together, the water inflow prediction and optimal planning of production define energy security, ensure the possibility of protection from floods, and eliminate idle discharges at hydroelectric power plants. The solution of such problems is complicated by lack of reliable information about the water inflow, its being stochastic in nature, a variable electricity consumption pattern, and inaccurate prediction and planning models. Improvement of prediction accuracy is focused on determining the water inventory for planning prospective electricity generation at HPPs taking into account regulation in the medium term. Such regulation is necessary to meet the power system load in the load curve peak and semi-peak parts. The paper considers the problem of constructing a medium-term water inflow prediction model for planning electricity generation for a week ahead taking into account climate changes in isolated operating power systems taking as an example the electric power systems of the Gorno-Badakhshan autonomous oblast in Tajikistan. For taking into account constant climate changes, it is proposed to use an approach based on machine learning methods, which features a self-adaptation capability. Based on the results of accomplished experimental and industry-grade numerical analyses, the expediency of using a model based on an ensemble of regression decision trees has been shown.

Impact of electric vehicles and realistic dish‐Stirling solar thermal system on combined voltage and frequency regulation of multiarea hydrothermal system

AbstractThe effective and reliable operation of an interconnected power system (PS) is a major concern, because of the large amount of power contribution from the intermittent renewable energy sources to the power grid. Energy storage (ES) devices are employed to enhance the reliability of the PS operation. Electric vehicles (EVs) are a novel type of distributed ES device that may be utilized to compensate for power imbalances. This paper discusses the participation of EVs for combined frequency and voltage control under conventional PS along with a thermal, hydro, realistic dish‐Stirling solar thermal system (RDSTS). Appropriate physical constraints such as governor dead band and generation rate constraints are equipped with thermal and hydro plants, and communication delay between the generating unit and load dispatch center is considered. An aggregate EVs model, RDSTS, and a new cascade controller named fractional‐order proportional‐derivative with filter coefficient cascaded with tilt‐integral‐derivative (CFOPDN‐TID) controller are provided in all areas for various studies. An artificial flora algorithm is employed to determine the optimal value of the various controller parameters under several scenarios. The system dynamics of the CFOPDN‐TID controller outperform the proportional‐integral‐derivative (PID) and TID controllers under various conditions. The incorporation of EVs improves the system performance in terms of less settling time, and peak deviation, and reduce oscillations. The system performance degrades with increases in the communication time delay of the EVs. The integration of RDSTS into the system provides better dynamics than the dish‐Stirling solar thermal system. Numerous studies have been conducted to validate the robustness of the proposed CFOPDN‐TID controller.

A [formula omitted] order Active Disturbance Rejection Controller for coordinated frequency-voltage control of deregulated hybrid power system with optimal electric-vehicle integration

The emergence of deregulation delivers a reliable operation of power system due to the fact that deregulated concept encourages competition among different power companies, thus avoiding monopoly. The present paper extends the idea of deregulation for a two-area hybrid power system establishing concurrent frequency-voltage control. The deregulated power system under consideration involves conventional-thermal, solar-thermal, wind-sources with appropriate non-linearities and present day electric-vehicles (EVs). This paper introduces a novel study showing the effects of large penetration of EVs with the utility grid. Furthermore, the optimal number of connected EVs for improved power system performance is examined.For establishing effective control, a 2nd order Active Disturbance Rejection-Controller (ADRC) with extended-state-observer is modelled as secondary controller. Magnetotacticbacteria optimization technique is used to determine the optimal parameter values. Simulation results depict a major reduction in the voltage and frequency deviations for proposed ADRC strategy with respect to industrially applied controllers. Also, a considerable reduction in steady state error values are obtained. Moreover, the ADRC gains prove to be robust enough under varying system conditions. The present study makes a way for further research on ADRC as a market ready controller.

Developing a Marine Predator Algorithm for Optimal Power Flow Analysis considering Uncertainty of Renewable Energy Sources

Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate.

Defect Analysis and Process Improvement of Metal-Oxide Arrester

Metal-Oxide Arresters are important equipment to ensure the reliable operation of power systems. An experimental protocol for judging the nature of MOA defects accurately is introduced in this paper and applied to engineering practice. Through diagnostic tests and dismantling inspections, it is determined that a certain batch of 110kV line MOA has a familial defect of poor sealing. Then, the reasons for dampness are analyzed, and other defects except the dampness of the varistor are discovered. Besides, the improvement of manufacturing process is proposed from aspects of terminal structure and the coating material on the side of varistor;the actual data proves that adding fillers such as quartz sand or silica gel inside the MOA can reduce the failure rate effectively. This article provides a certain technical reference for the manufacture and defect disposal of MOAs.

Severity Evaluation of UHF Signals of Partial Discharge in GIS Based on Semantic Analysis

The severity evaluation of UHF signals of partial discharge in GIS is helpful to formulate a maintenance strategy in time, and improve the reliability of power system operation. Most of the evaluation methods are based on structured data, which cannot fully characterize the state of the equipment. In this paper, a method for evaluating the severity of UHF signals of partial discharge in GIS based on semantic analysis is presented. It comprehensively considers the influence of structured data and unstructured text data on the state of equipment, including measured data on partial discharge, relevant information of defects, and equipment operating parameters. Firstly, the severity of UHF signals of partial discharge is defined. According to the on-site detection of substations, a data set containing equipment detection reports and PRPS data is established. Aiming at semantic analysis, Word Embedding is used to associate and encode textual information to reduce the subjectivity of the encoding. Measured data on partial discharge is also combined and analyzed for evaluation. The method proposed is used for case analysis and compared with other methods. The results show that the comprehensive utilization of structured data and unstructured text data can reflect the state of equipment more comprehensively and truthfully, and improve the accuracy of the results.

Short‐term commercial load forecasting based on peak‐valley features with the TSA‐ELM model

AbstractCommercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a short‐term commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak‐valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24‐h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA‐ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC ≥ 0.8 features are added to the TSA‐ELM to achieve short‐term commercial electricity load forecasting. The results show that the PV (Peak & Valley)‐TSA‐ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV‐TSA‐ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method.

A fast and accurate Bi-level fault location method for transmission system using single ended phasor measurements

To guarantee continuous and reliable operation of power systems working near their stability margins, it is necessary to clear faults quickly. For this purpose, accurate and fast fault location methods are required. Single-ended phasor based methods are a category of fault location strategies which are favourable due to simplicity of their implementation. However, enhancing the accuracy of these methods is a challenging task due to unavailability of data from the remote end of the line. In this paper, a novel fault location method that requires the measured data of one end of the line is proposed. As a result, this method does not need communication links. The proposed method is bi-level and in its first level, fault characteristics such as current, voltage, and impedance of the fault are calculated for a given fault location obtained from Level-2. Then, based on the calculated fault characteristics, fault location is updated in the second level. The presented fault location method considers magnetic coupling between lines and it is also applicable for both transposed and un-transposed lines. Numerous simulation studies verify the accuracy of the proposed method in presence of uncertainties such as noise, fault impedance and error in input line parameters. Furthermore, although the proposed method is based on a bi-level iterative approach, the presented results confirm that it converges quickly in 2 or 3 iterations.

Impact of implementing a price-based demand response program on the system reliability in security-constrained unit commitment problem coupled with wind farms in the presence of contingencies

Large scale penetration of highly intermittent wind energy sources has caused new challenges to the reliable operation of power systems. To address these challenges, demand response program is considered as an efficient solution to manage shift-able loads. In this paper, the impact of demand response on power system reliability has been evaluated by developing a two-stage stochastic security constraint unit commitment that considers both the wind volatility and line outages at the same time. The allocation of ramping products and spinning reserves to cover these uncertainties has also been modeled. wind curtailment and load shedding are considered as alternative strategies, and an optimal exchange between them and the use of spinning reserves and flexible ramping products has been modeled in the objective function. To assess the system reliability, the expected load not served (ELNS) index was evaluated in different cases. The proposed scheme as a mixed-integer linear programming problem was solved by a scenario-based two-stage stochastic programming method. The IEEE test systems 6-bus, modified 24-bus, and 118-bus were examined numerically. The results show that the simultaneous consideration of demand response and flexible ramping product coordinated with wind energy has adequate flexibility to reduce the reliability index and system operational cost.

Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast

Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system.

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion.

The frequent occurrence of single-phase grounding faults affects the reliable operation of power systems. When a single-phase grounding fault occurs, it is difficult to accurately identify the fault type because of the weak characterization and subtle distinction between different fault types. Therefore, this paper proposes a single-phase grounding fault type identification method based on the multi-feature transformation and fusion. Firstly, the Hilbert–Huang transform (HHT) was used to preprocess the fault recorded wave data to highlight the characteristics between different fault types. Secondly, the deep learning model ResNet18 and the long short-term memory (LSTM) are designed to extract the complex abstract features and time-series correlation features from the preprocessed data set separately. Finally, it designs a fusion model to combine the advantages of heterogeneous models to identify the type of single-phase grounding fault. Experiments validate that the method is good at fully mining the characteristics of the fault types contained in the fault recorded wave data, so it can identify multiple types of faults with strong robustness and provide a reliable basis for the subsequent formulation of targeted fault-handling measures.

RETRACTED: Control of Bidirectional DC-DC Converter for Micro-Energy Grid’s DC Feeders' Power Flow Application

Referred to by: Retraction notice to ‘Control of Bidirectional DC-DC Converter for Micro-Energy Grid’s DC Feeders' Power Flow Application' IJRED 11(2), 533-546International Journal of Renewable Energy Development, 12(6). https://doi.org/10.14710/ijred.2023.57139

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interests active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which significantly enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

Forecast Competition in Energy Imbalance Market

Uncertainties in renewable generation make accurate load forecast essential for reliable power system operation. This paper considers energy imbalance market (EIM), where market players are allowed to procure energy ahead of time and trade the mismatch due to forecast error and strategic behaviors. The ISO sets the trading prices according to the market conditions, and pursues various system-level objectives. We first identify the power-law relationship between data volume and forecast accuracy, which enables the formulation of forecast cost model. Then, we cast the interactions in the EIM in the Stackelberg game framework with the ISO acting as the leader. We offer explicit subgame perfect equilibrium among the players in EIM, and derive the sufficient condition for the existence of unique equilibrium. Then, we show that this equilibrium, if exists, supports the maximal social welfare and under certain conditions, minimizes the total mismatch. We further examine the local and global impacts of the forecast errors under mild conditions, together with robustness analysis. Such analysis provides mechanism design guidelines for the ISO to enable the data sharing and forecast method sharing among market players in the EIM. Numerical studies further examine the effectiveness, robustness and sensitivity of the subgame.

Modelling of renewable energy power plant controllers for steady-state studies using an extended power flow formulation

The increasing penetration of large-scale wind and photovoltaic plants has created new challenges for the secure and reliable operation of power systems. To face these challenges, these renewable plants are composed of numerous inverter-based resources that use centralized controllers to satisfy grid code requirements related to frequency and voltage control, both under dynamic and steady-state conditions. In this paper, an extended power flow formulation is proposed for the steady-state modelling of power plant controllers along with their power flow controls including those associated with droop control, suitable for any plant topology and number of regulation devices. The effectiveness of this modelling approach is validated against WECC generic models using dynamic simulations in PSS®E. For this purpose, a 13-bus 5-generator test system representing a 100-MW renewable power plant is studied. The accuracy of the introduced formulation is demonstrated since it presents absolute errors smaller than 0.2% between both fundamentally different methods. Additionally, the well-known IEEE RTS-96 test system is used to model 29 power plant controllers with 93 generators, thus showing the practicality of the proposed formulation. It can be envisaged that this timely tool is valuable for power system operation and expansion planning considering large-scale variable renewable energy power plants.

A Deep Attention Convolutional Recurrent Network Assisted by K-Shape Clustering and Enhanced Memory for Short Term Wind Speed Predictions

Due to the increasing penetration of wind energy in nowadays power grids, the accurate wind speed prediction (WSP) is critical to more efficient and reliable operations of power systems containing wind turbines. This paper presents a novel deep attention convolutional recurrent network with K-Shape and enhanced memory (DACRN-KM) for more accurate short-term WSP. To well capture spatial-temporal information among wind speeds measured across the wind farm, a DACRN is firstly developed to extract latent representations. Next, an auto-updated memory module is developed to rebuild latent representations based on historical records. A K-shape clustering algorithm is applied to derive K patterns of rebuilt latent representations and the final prediction layer (FPL) is developed to generate the WSP result for latent representations assigned to one out of K patterns each time. The effectiveness of the proposed DACRN-KM is validated with data collected from 25 wind turbines (WTs) in multiple wind farms, in which a part of them is publicly accessible, by benchmarking against a set of the commonly considered and recently reported methods.

Research on Short-term Wind Power Forecasting Based on Stacking Integrated Model

With the large-scale use of wind farms, wind power has become an indispensable energy source. However, due to the influence of internal instability factors of wind, it poses a huge threat to the economic efficiency and reliability of power system operation. In order to solve this problem, according to the random volatility of wind power, this paper establishes a wavelet threshold noise reduction model to process wind speed characteristics. Because the wind power is very different when the wind speed is the same, many of the wind turbine’s own attributes are added to the input characteristics. Based on the strong generalization ability of decision tree models, the gradient descent tree, extreme gradient boost tree, and lightGBM algorithm are used as the base learner, and the multiple linear regression model is used as the meta-learner to construct a stacking integrated learning model. Compared with methods such as long and short-term memory neural networks, the performance and generalization ability of the cascading generalization algorithm combination model is relatively good.