Historical Power Research Articles

Load Forecasting using ANN

Accurate load forecasting plays a key role in economical use of energy. Artificial Neural Network (ANN) models have been extensively implemented to produce accurate results for short-term load forecasting with time lead ranging from an hour to a week. In this report daily peak load forecasting has been performed for the part of a town supplied by 2 distribution feeders on weekdays by taking into consideration the historical maximum Power consumption in MWH, Voltage in KV and Current in Amp data. Optimization of the network parameters is performed for both learning rules. Energy demand forecasting is of great importance in the management of power systems. In this report artificial neural network technique (ANN) is used for forecasting the load curve. Algorithms using these techniques have been programmed using MATLAB 15 and applied to the case study. The efficiency of both the model is determined from the load curve and the load is predicted as a testing sample. The ANN model trains the daily load data for a set of days and then forecast the load for next day. Actual data are obtained from "Mankapur Substation" is used to validate the result. Keywords: Load forecasting; Load demand; Power Quality; MATLAB; Load

Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm

The large-scale integration of wind power to the grid poses some potential challenges to the power system. Accurate wind power forecasts reduce the impact of the nonlinearities and volatility of wind power generation. A two-channel deep learning model based on an improved coot optimization algorithm (ICOA) is proposed. First, the use of long short-term memory (LSTM) builds a channel for the extraction of chronological characteristics of historical power. Second, a temporal convolutional network (TCN) is adopted as a hybrid feature-extraction channel for multi-dimensional meteorological data, and by incorporating the self-attention mechanism (SA), the ability of TCN to extract internal information from meteorological features is enhanced. Finally, the ICOA that introduces nonlinear decision factor, adaptive dynamic boundary, and Cauchy mutation is used to optimize the model hyperparameters. The simulation analysis is carried out on the winter and summer measured data of a wind farm in Xinjiang. The results show that compared with the traditional LSTM model, the root mean square error and the mean absolute error of the proposed method are reduced by 10.35 % and 16.27 % on average, respectively, and the prediction accuracy is higher than that of other comparative models, which verifies the superiority of our proposed model.

A review of authorship in herpes simplex virus type-2 (HSV-2) research conducted in low-income and middle-income countries between 2000 and 2020

IntroductionEquitable inclusion of low-income and middle-income country (LMIC) researchers and women in research authorship is a priority. A review of progress in addressing WHO-identified priorities provided an opportunity to examine...

A Fault Prediction Method for Secondary Electric Power Equipment Based on Multi-Round Undersampling Random Forest

Abstract It is necessary to predict the fault states of the large number of power secondary equipment using intelligent methods. However, when compared to large-scale equipment, the number of negative samples representing faulty equipment is significantly smaller than the number of positive samples representing normal equipment. This leads to a pronounced imbalance between positive and negative samples in the task of fault prediction. In this paper, we propose a multi-round undersampling random forest method to predict the fault situations of secondary electric power equipment. First, we collect data from historical power system logs to build the dataset for power secondary equipment and preprocess it. The undersampling method is utilized to generate a balanced dataset of secondary power equipment with a smaller sample size. We generate multiple balanced datasets through rounds of random undersampling without replacement to train multiple random forest models. Subsequently, we predict fault situations in secondary electric power equipment through comprehensive decision-making by these multiple random forest models. We employ a real dataset from the power system of Chongqing, China, for experimental validation. The results demonstrate the superiority of our method over other machine learning prediction models used for comparison.

Parameter Estimation and Preliminary Fault Diagnosis for Photovoltaic Modules Using a Three-Diode Model

Accurate estimation of photovoltaic (PV) power generation can ensure the stability of regional voltage control, provide a smooth PV output voltage and reduce the impact on power systems with many PV units. The internal parameters of solar cells that affect their PV power output may change over a period of operation and must be re-estimated to produce a power output close to the actual value. To accurately estimate the power output for PV modules, a three-diode model is used to simulate the PV power generation. The three-diode model is more accurate but more complex than single-diode and two-diode models. Different from the traditional methods, the 9 parameters of the three-diode model are transformed into 16 parameters to further provide more refined estimates. To accurately estimate the 16 parameters in the model, an optimization tool that combines enhanced swarm intelligence (ESI) algorithms and the dynamic crowing distance (DCD) index is used based on actual historical PV power data and the associated weather information. When the 16 parameters for a three-diode model are accurately estimated, the I–V (current-voltage) curves for different solar irradiances are plotted, and the possible failures of PV modules can be predicted at an early stage. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different ESI algorithms are compared for different weather conditions. The results affirm the reliability of the proposed ESI algorithms and the value of creating more refined estimation models with more parameters. Preliminary fault diagnosis results based on the differences between the actual and estimated I–V curves are provided to operators for early maintenance reference.

Estimation of aggregated inertia constant and load damping: A PMU-based analytical approach

A precise, straightforward, and efficient data-driven methodology is introduced to estimate the most important aggregated generation-load parameters of the power system frequency response model i.e., the inertia constant and the load damping. The estimation method depends on defining specific base systems characterized by particular frequency features. A sensitivity analysis is carried out to determine the most suitable base system for each real-time application. The data collected from PMUs is then compared to the attributes of the base system to make accurate estimations of the parameters of interest. The effectiveness of the proposed methodologies is assessed using datasets that include both simulated data from the New York-New England benchmark and actual measurements collected from six historical power system disturbances within a national power grid.

Changes in Our Government's Counterpart Support Policy, Logic of Evolution and Prospects for Reform

Coordinated regional development is inextricably linked to the practice of the counterpart support policy, and a scientific understanding of its historical process, evolutionary logic, and power mechanism is an important issue in realizing comprehensive economic and social progress and common prosperity. Based on the perspective of historical institutionalism, a panoramic overview of the internal logic of the change of the counterpart support system can help improve the foresight of reform. China's counterpart support system has experienced three stages: early germination, initial establishment and deep promotion, and the reform process has been deeply influenced by the transformation of the economic system, the requirements of regional coordinated development and the consensus of community values, and has shown obvious path dependence. The birth of the "factory-package commune" model, the convening of the National Conference on Border Defence, and the speeches made at the conference on post-disaster reconstruction are three key points in the development of the counterpart support system. The power mechanism for the evolution of the counterpart support system comes from the vertical "administrative contracting system" and the horizontal "competition tournament". In the future, the counterpart support system will continue to develop along the lines of scientific, intelligent and balanced development, exploring new modes of counterpart support, and pushing forward the improvement of the quality and effectiveness of counterpart support work.

Long-Term Solar Power Generation Forecasting at Eastern West Large Scale Solar (LSS) Farm using Random Forest Regression (RFR) Method

Precise forecasting of power generation and demand is essential for effective resource allocation and energy trading in contemporary energy systems. Power forecasting accuracy has increased dramatically since Random Forest Regression (RFR) techniques were used. The study's primary objective is to forecast electricity generation in Malaysia's Eastern West region, with a concentration on solar energy. The research process entails gathering and examining pertinent factors, weather information, and historical power data. To evaluate the accuracy and predictive potential of RFR models, a specific power grid is used for training, validation, and testing. One of the anticipated results is the creation of an accurate model for power generation predictions, which will help to optimise energy operations and smoothly incorporate renewable sources. The paper examines the advantages, disadvantages, and best practices related to RFR-based power forecasting. The dataset, which spans the years 2019 to 2023, includes 30-minute interval records for the following variables: average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed. Using the RandomForestRegressor class from the scikit-learn library, the RFR model is implemented. In order to assess the model's overall fit, average deviation, and sensitivity to outliers, measures such as root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) are used on the test set. The temperature, irradiance, and AC power output of PV modules are found to be strongly correlated.

Setting a Better Default: Designing a Welcome Academy for New Faculty Centered on Inclusive Teaching in Engineering

This design case describes a Welcome Academy for New Faculty in Engineering. To situate the design, this work is mo- tivated by the documented need to make STEM education more inclusive. This need has prompted extensive research on best practices for inclusive teaching, but less is known about how to translate that research into actual teaching practice. This design case addresses that difficulty. Influenced by Thaler and Sunstein’s theory of nudging, the Welcome Academy resets the default to expect inclusive teaching. To develop the design, we organized an off-campus summit to solicit input from current engineering faculty on the question, “What do new engineering faculty need to know about diversity, equity, and inclusion (DEI)?” That input guided the creation of a four-hour workshop, delivered the morning after campus-wide new faculty orientation, that included an icebreaker, basic campus demographics, curated DEI-related resources, a campus tour emphasizing historical power dy- namics, and presentations by current engineering students. To depict the experience of the design, we describe the final implementation, which varied from the design at points, and the unanimously positive feedback from new faculty. That feedback, however, was not the result of a flawless implementation: We also describe a number of failures that will improve subsequent iterations of the Welcome Academy, emphasizing the importance of communication, respect, and flexibility.

Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks

Wind energy is a clean resource widely utilized as a renewable energy source. However, due to its inherent strong volatility and the multitude of influencing factors, it is challenging to accurately predict wind power. To address these issues, an IVMD-LASSO-BiGRU model, comprising Improved Variational Mode Decomposition (IVMD), Least Absolute Shrinkage and Selection Operator (LASSO), and Bidirectional Gated Recurrent Unit (BiGRU), is proposed for forecasting. Firstly, based on the sparse prior knowledge of each component constructed in the variational model by VMD, the optimal decomposition mode number K is determined at the inflexion point where the sparsity index shifts from rising to falling. The original wind power sequence is then decomposed into a series of Intrinsic Mode Functions (IMFs) using VMD with the optimal K value, thereby reducing the volatility of the original sequence. Secondly, LASSO is employed to select key features from meteorological data, historical wind power, and IMFs, thereby reducing the data dimension. Subsequently, BiGRU is utilized to fully extract the temporal features of the input data, establishing the mapping between input and output. Experimental results demonstrate that on three different datasets, the R2 values of the proposed forecasting method reach 0.9872, 0.9917, and 0.9941, respectively. Compared to the traditional BiGRU model, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced by an average of 57.56% and 58.88%, respectively. Thus, it is evident that the proposed method enhances the accuracy of short-term wind power forecasting, providing a basis for adjusting power generation plans.

Clive Palmer’s claims against Australia for billions renew pressure to remove investor rights to sue governments from trade agreements

AbstractThe Australian debate over Investor-State Dispute Settlement (ISDS) sharpened in 2023 because Australian mining billionaire Clive Palmer, having previously registered his mining company in Singapore, has claimed to be a Singaporean investor. He is using ISDS provisions in the 2012 ASEAN-Australia-New Zealand Free Trade Agreement and the amended 2017 Singapore-Australia Free Trade Agreement to sue the Australian government for a total of A$410 billion in three separate claims. This article uses Cox’s critical theory framework developed by Schneiderman to explain the historical development and power dynamics of ISDS, the contest between its business supporters and social movement critics, and the impact of this contest on governments. It then analyses the Palmer claims and explores the global debate about ISDS, including its increased use by fossil fuel companies against government regulation of carbon emissions, which has led to increased resistance from social movements and governments. ISDS is also being reviewed by the United Nations and World Bank institutions which provide arbitrators for its tribunals and by the Organisation for Economic Co-operation and Development. The conclusion assesses the debate over whether ISDS can be reformed and its future viability.

Role of Mediation in Territorial Disputes in East Asia

Purpose: The aim of the study was to analyze the role of mediation in territorial disputes in East Asia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: In East Asia, mediation plays a pivotal role in addressing territorial disputes by fostering diplomatic dialogue and negotiation. It aims to de-escalate tensions and find mutually acceptable solutions, preventing conflicts from worsening. Despite challenges like historical animosities and power imbalances, mediation remains a crucial tool for managing territorial disputes in the region. Unique Contribution to Theory, Practice and Policy: Structural realism, constructivism & regional institutionalist theory may be used to anchor future studies on mediation in territorial disputes in East Asia. Mediation offers a constructive mechanism for de-escalating tensions and facilitating dialogue between disputing parties. Promoting mediation as a preferred method for resolving territorial disputes in East Asia can enhance regional peacebuilding efforts and promote a rules-based international order.

Site-specific production forecast through data-driven and engineering models

The present study investigates the predictive capabilities of two site-specific wind power forecasting models. The two approaches make use of a machine learning ensemble model that combines forecasts from several numerical weather predictions (NWP) to match local observations. In the first approach, the training data consists of historical site power production recordings. This yields a black-box model that includes the wind farm behaviour. The second approach is a combination of data-driven and explicit modelling. In the first step, the machine learning ensemble model forecasts the site wind conditions. Then, this input is fed to a site-specific engineering wake model. This hybrid ”grey-box” approach explicitly resolves some farm flow effects.The study shows that the performance of both methods differs over the wind speed range. The purely data-driven model performs better at medium wind speeds, which also occur more often. The hybrid method predictions agree better during periods of higher wind speeds. In the last part, the study showcases the capabilities of the forecasting methods when participating in the energy market. Additionally, the engineering wake model allows for power maximisation through wind farm control.

Short-term integrated forecasting method for wind power, solar power, and system load based on variable attention mechanism and multi-task learning

Improving the forecasting accuracy of wind power, solar power, and system load to support the source-load cooperative dispatch is an important direction to reduce the uncertainty at source and load sides. The current research mainly focuses on a single object, ignoring the interactive coupling relationship among them, which limits the improvement of forecasting accuracy. Therefore, this paper proposes a short-term integrated forecasting method of wind-solar-load. Firstly, a feature extraction module of linkage characteristics of wind-solar-load is built based on variable attention mechanism. Secondly, a multi-task learning model that can automatically calculate the optimal loss weights for different forecasting tasks is constructed to simultaneously accomplish the wind and solar power forecasting tasks through Fully Connected Neural Network. Finally, a load forecasting model which fuses historical load and power forecasting information is established based on Long Short-Term Memory. The operation data of 8 wind farms and 6 solar plants, and the load data of a nearby city are used for instance analysis. The results show that the power forecasting error (root mean square error) of each wind farm, solar plant, and system load is reduced by 4.84 %, 1.86 %, and 3.02 % on average, respectively, compared with the corresponding traditional methods.

Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method

Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.

Stochastic and Extreme Scenario Generation of Wind Power and Supply–Demand Balance Analysis Considering Wind Power–Temperature Correlation

In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand balance analysis based on it. Firstly, the influence of temperature on wind power output is analyzed via Pearson coefficient to obtain the correlation between wind power and temperature. Secondly, based on the historical wind power curve, a large number of wind power output scenarios are randomly generated while fully preserving its characteristics, and probabilistic supply–demand analysis is carried out. Thirdly, for the extreme case of continuous multi-day extreme heat without wind, extreme scenarios are selected from the generated scenarios for supply–demand balance analysis. Finally, a practical example in a province in central-eastern China is used to verify the effectiveness of the proposed method. The results indicate that the scenario generation method can effectively capture the historical wind power characteristics and can be better applied to the diversified supply and demand balance analysis to obtain more accurate analysis results.

Participatory translation and anti‐racism in NGO development work: A method of co‐producing translations with community members

AbstractMotivationThis article argues that the NGO sector should prioritize translation as an anti‐racist practice because failing to tackle colonial language hierarchies replicates historic power structures. Aligned with the locally led approach to development, the article responds to calls for more research into the role of translation in shaping development outcomes, and answers direct appeals from NGO practitioners for translation glossaries in different languages to improve communication between development actors.PurposeWe introduce a new participatory method for co‐producing translations of terms that community members deem important for their visions of “development” and social change. We contend that participatory translation fosters an anti‐racist approach to knowledge production by centring communities in the construction of meaning, which may encompass language and concepts outside the international development lexicon.Approach and methodsThe study was based on the principles of community‐based participatory research, and an Advisory Board of community members was instrumental in its design. The draft of the glossary was produced in two three‐day participatory workshops in Lilongwe and Zomba, Malawi. These were attended by 36 people representing potential user groups of the glossary.FindingsThe workshop participants created 385 translations of development terms, including 70 translations that are not listed in the Oxford Chichewa–English Dictionary. They also engaged in critically reflective discussions that challenged dominant discourses of development. We argue that participatory translation is a tool to transcend the language barrier in a way that simultaneously subverts conventional power hierarchies and offers access to different ways of understanding the world.Policy implicationsNGOs in different linguistic and geographical contexts could adopt participatory translation activities in the early stages of forming relationships with communities and local partners to build trust and common understanding. In line with an anti‐racist approach, this would help to combat systemic linguistic exclusion, which exacerbates other forms of disadvantage.

A double helix of critical inquiry: dialogic methods of critical pragmatism

ABSTRACT In this essay, we propose a double helix of critical inquiry; one that binds both critical theory and appreciative inquiry through critical pragmatism. We first situate the double helix within the paradigmatic, theoretical, and praxeological commitments of the critical paradigm followed by anchoring dialogue as a methodology that activates the double helix through pragmatism. We then introduce three specific methods of dialogue that bring to life how critical communication scholars can use dialogue as a means for socially situating dialogue within historical power dynamics for envisioning and creating social change. We conclude with theoretical and practical implications for communication scholars to consider when conducting critical communication research and teaching this area of inquiry in their communication courses. Our hope is that this framework of a double helix produces communication inquiry that engages communities in the cocreation of a just society.

Renewable energy sources integrated load frequency control of power system: A review

Due to advancements in human society and lifestyle changes, the escalating demand for electric energy, along with the expansion of industrialization, highlights the growing importance of green energy such as renewable energy sources (RESs). Nevertheless, the combination of these sources introduces various operational and control challenges for existing power systems. A pivotal concern in power systems involves achieving the essential load frequency control (LFC) to mitigate the deviations in system frequency and unscheduled power exchanges among control areas. This paper conducts a thorough review of the progression of LFC research, spanning historical, present, and prospective power systems. The analysis encompasses considerations of traditional power system models, the integration of RESs into traditional models, associated constraints, deregulated LFC, the application of power electronics devices, energy storage implementations, and LFC strategies tailored for microgrids or multi-microgrids. This comprehensive literature review aims to bridge the research gap by addressing contemporary challenges, such as uncertainties in RES development, and provides insights into future directions for LFC of the power systems.

Research on power generation prediction of hydropower in river basin based on multi-head attention graph convolutional neural network

For the power generation prediction of traditional hydropower stations, most of them only use time series prediction and neglect to study the spatial topological relationship of hydropower stations in the river basin, so that it is difficult to fully explore the characteristic relationship of space power stations. In this paper, a research method for power generation prediction of hydropower in river basin hydropower stations based on multi-head attention map convolutional neural network is proposed. This method establishes a first-level node neighborhood feature map based on the spatial geographic distribution information of hydropower stations in the basin, and uses the method of graph convolution to carry out node feature mining and feature learning, so as to transform the power generation capacity evaluation problem of the hydropower station in the basin into the node prediction problem in the graph, which is different from the global normalization rule. the multi-head attention mechanism introduced further improves the information aggregation quality of the graph node, and uses the historical temperature, power generation, electricity price, unit status and other data of each hydropower station in the basin for training. The reasoning results show that the proposed method achieves higher accuracy than other schemes, and the power prediction method is conducive to the formulation of power plans of hydropower stations in the basin, and can also play a positive role in guiding the site selection of hydropower plants.