Wind speed interval prediction is playing an increasingly important role in wind power production. The intermittent and fluctuant characteristics of wind power make high-quality prediction interval challenging. In this paper, a novel hybrid model based on a gated recurrent unit neural network and variational mode decomposition is proposed for wind speed interval prediction. Initially, variational mode decomposition is employed to decompose the complex wind speed time series into simplified modes. Interval prediction model and a point prediction model based on a gated recurrent unit neural network are designed to conduct interval prediction in primary mode and point prediction in rest modes, respectively, before the composition and construction of the prediction interval. Then, an error prediction model based on a gated recurrent unit neural network is proposed to enhance the model performance by error correction. Eight cases from two wind fields are used to test and verify the proposed method. The results indicate that the proposed method is a highly qualified method that has a much higher prediction interval coverage probability and narrower prediction interval width.

Short-Term Wind Speed Interval Prediction Based on Ensemble GRU Model

Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks

The electrical power system has experienced several changes during the last decade, raised by continuously increasing load demand, rapid depletion in fossil fuels, and newly electrical deregulation policy. In the past, numerous review literatures has been published in the field of Load Frequency Control (LFC), which deals with different and recent control strategies for the successful operation of the power system. Moreover, due to changes in lifestyle, increasing load demand, expansion of industrialization, and environmental issues, Renewable Energy Sources (RES) integration becomes the obvious adaptive choice. Since generation from RES is stochastic in nature and depends upon the weather condition and other aspects at every instant of time. Therefore, high penetration may reflect specific issues regarding voltage instability, frequency stabilization, and reliability obstruction. Hence this issue has been covered effectively in this review work. Furthermore, the work precisely summarized and briefly explained the different scenarios of Energy Storage (ES), micro-grid and Flexible AC Transmission System (FACTS) to explore the possible solutions and future aspects. The merit and demerit of different controllers are also investigated with the help of a comparison's tables and also some other analytical comparisons. The overall study examines an in-depth review of recent technical core aspects for LFC based on classical and modern power system which involves the non-linear model, controller design parameters, soft-computing application, attributes of Load forecasting, as well as integration of RES in a deregulated environment. This effective, comprehensive literature survey is very helpful for researchers to bridge the gap between recent development, implementation, challenges and future trends of RES in LFC. 

A literature survey on load frequency control considering renewable energy integration in power system: Recent trends and future prospects

In this paper, a novel energy storage method based on pumped hydropower energy storage (PHES) for a renewable energy integrated micro-grid (REMG) is proposed, and the load frequency control (LFC) for the system is studied. In a typical REMG, micro pumped storage units are built that rely on a tall building to convert energy by pumping water up to store energy and releasing the stored water to generate energy. Because of the fluctuation of renewable energy (RE) and the perturbation of the load demand, frequency deviation, and tie-line power interchange are inevitable. In this paper, the LFC controller optimization problem for the REMG is investigated, and the optimal controllers for multi-areas in the REMG are designed. To solve the optimization problem of LFC, a novel meta-heuristic algorithm called artificial sheep algorithm (ASA) is applied in the task of LFC optimization. In the experiments, RE scenarios of different seasons, the impact of PHES, and the control robustness are studied. The results not only prove the feasibility of the proposed REMG but also show the effectiveness of the optimized controllers in maintaining frequency stability under various conditions.

Load Frequency Control of a Novel Renewable Energy Integrated Micro-Grid Containing Pumped Hydropower Energy Storage

State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems

A nonlinear generalized predictive control for pumped storage unit

Pumped storage units (PSUs) are an important storage tool for power systems containing large-scale renewable energy, and the merit of rapid start-up enable PSUs to modulate and stabilize the power system. In this paper, PSU start-up strategies have been studied and a new integrated start-up method has been proposed for the purpose of achieving swift and smooth start-up. A two-phase closed-loop startup strategy, composed of switching Proportion Integration (PI) and Proportion Integration Differentiation (PID) controller is designed, and an integrated optimization scheme is proposed for a synchronous optimization of the parameters in the strategy. To enhance the optimization performance, a novel meta-heuristic called Artificial Sheep Algorithm (ASA) is proposed and applied to solve the optimization task after a sufficient verification with seven popular meta-heuristic algorithms and 13 typical benchmark functions. Simulation model has been built for a China’s PSU and comparative experiments are conducted to evaluate the proposed integrated method. Results show that the start-up performance could be significantly improved on both indices on overshoot and start-up, and up to 34%-time consumption has been reduced under different working condition. The significant improvements on start-up of PSU is interesting and meaning for further application on real unit.

An Integrated Start-Up Method for Pumped Storage Units Based on a Novel Artificial Sheep Algorithm

The invention discloses a method for intelligently starting a water turbine of a pump storage unit under a working condition. The method comprises the steps of (1) building a pump turbine adjustment system simulation model of the pump storage unit; (2) setting a three-stage startup control principle in the pump turbine adjustment system simulation model; (3) establishing a startup process control parameter optimization target function by adopting an index of an integral of discrete time multiplied by an error absolute value as a target function of control parameter optimization according to the three-stage startup control principle; and (4) solving the startup process control parameter optimization target function by applying a heuristic optimization method, and obtaining optimal control parameters. According to the method, the control parameters are optimized and applied to simulation calculation, so that the working-condition startup quality of the water turbine of the pump storage unit under different water heads is remarkably improved, the startup time is shortened, the overshoot is reduced, and indexes of speed fluctuation and the like are reduced.

Method for intelligently starting water turbine of pump storage unit under working condition

The invention discloses a double-objective optimization method and system for a pumped storage unit starting rule, and is used for optimizing a decision variable when a pumped storage unit water turbine is subjected to working condition starting. The method specifically comprises the following steps that: according to a pumped storage unit regulation system, establishing a simulation model; takingrevolving speed overshoot and revolving speed raising time, establishing a double-objective optimization function; under a constraint condition of the decision variable, a revolving speed curve and atarget function, carrying out iterative solving a pumped storage unit regulation system simulation model, and obtaining an optimal guide vane starting rule set. By use of the method, the evolving speed overshoot and the revolving speed raising time are selected as to optimization objectives, one group of optimal solutions can be searched, more selection choices are provided for decision makers, and a solution selected through decision represents an optimal starting rule. Optimal starting rule can make the working condition starting time of the pumped storage unit water turbine shorter, the revolving speed overshoot of the unit is smaller, the revolving speed regulation time of the unit is shorter, and the dynamic characteristic quality of a system transition process is better.

Double-objective optimization method and system for pumped storage unit starting rule

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