Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods

With the increasing proportion of renewable energy in China’s energy structure,among which photovoltaic power generation is also developing rapidly.As the photovoltaic(PV)power output is highly unstable and subject to a variety of factors,it brings great challenges to the stable operation and dispatch of the power grid.Therefore,accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy.Currently,the short-term prediction of PV power has received extensive attention and research,but the accuracy and precision of the prediction have to be further improved.Therefore,this paper reviews the PV power prediction methods from five aspects:influencing factors,evaluation indexes,prediction status,difficulties and future trends.Then summarizes the current difficulties in prediction based on an in-depth analysis of the current research status of physical methods based on the classification ofmodel features,statistical methods,artificial intelligence methods,and combinedmethods of prediction.Finally,the development trend ofPVpower generation prediction technology and possible future research directions are envisioned.

Application of Model Predictive Control Based on Kalman Filter in Solar Collector Field of Solar Thermal Power Generation

There are two prominent features in the process of temperature control in solar collector field.Firstly,the dynamic model of solar collector field is nonlinear and complex,which needs to be simplified.Secondly,there are a lot of random and uncontrollable,measurable and unmeasurable disturbances in solar collector field.This paper uses Taylor formula and difference approximation method to design a dynamic matrix predictive control(DMC)by linearizing and discretizing the dynamic model of the solar collector field.In addition,the purpose of controlling the stability of the outlet solar field salt temperature is achieved by adjusting the mass flow of molten salt.In order to further improve the ability of the system to suppress unmeasured disturbances,a steady-state Kalman filter is designed to estimate state variables,so that the system has better stability and robustness.The simulation verification results show that the DMC control system based on Kamlan filtering has better control effect than the traditional DMC control system.In the case of large fluctuations in solar radiation intensity and consideration of undetectable interference,the overshoot of the system is reduced by 4%and the rise time remains unchanged.

A Control Strategy for Smoothing Active Power Fluctuation of Wind Farm with Flywheel Energy Storage System Based on Improved Wind Power Prediction Algorithm

The fluctuation of active power output of wind farm has many negative impacts on large-scale wind power integration into power grid. In this paper, flywheel energy storage system (FESS) was connected to AC side of the doubly-fed induction generator (DFIG) wind farm to realize smooth control of wind power output. Based on improved wind power prediction algorithm and wind speed-power curve modeling, a new smooth control strategy with the FESS was proposed. The requirement of power system dispatch for wind power prediction and flywheel rotor speed limit were taken into consideration during the process. While smoothing the wind power fluctuation, FESS can track short-term planned output of wind farm. It was demonstrated by quantitative analysis of simulation results that the proposed control strategy can smooth the active power fluctuation of wind farm effectively and thereby improve power quality of the power grid.

A comprehensive review on the development of data-driven methods for wind power prediction and AGC performance evaluation in wind–thermal bundled power systems

The wind–thermal bundled power system achieves energy complementarity and optimized scheduling, which is an important way to build a new type of energy system. For the safe and stable operation of the wind–thermal bundled power system, accurate data-driven analysis is necessary to maintain real-time balance between electricity supply and demand. By summarizing the development and characteristics of wind–thermal bundled power system in China and different countries, current research in this field can be clearly defined in two aspects: short-term wind power prediction for wind farms and performance evaluation of automatic generation control (AGC) for thermal power generation units. For short-term wind power prediction, it is recommended to focus on historical data preprocessing and artificial intelligence methods. The technical characteristics of different data-driven wind power prediction methods have been compared in detail. For performance evaluation of AGC units, a comprehensive analysis was conducted on current evaluation methods, including the “permitted-band” and “regulation mileage” methods, as well as the issue of evaluation failure in traditional evaluation methods in practical engineering. Finally, the relative optimal dynamic performance of AGC units was discussed and the future trend of data-driven research in wind–thermal bundled power system was summarized.

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

Research on Wind Farm Participation in Power Grid AGC Control

Since wind power has the features of being intermittent and unpredictable, and usually needs transmission over long distances, grid integration of large-scale wind power will exert signif icant influence on power grid planning and construction, and will make a heavy impact on the safe and reliable operation of power systems. To deal with the diff iculties of large scale wind power dispatch, this paper presents a new automatic generation control (AGC) scheme that involves the participation of wind farms. The scheme is based on ultra-short-term wind power forecast. The author establishes a generation output distribution optimization mode for the power system with wind farms and verif ies the feasibility of the scheme by an example.

Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power grid.Therefore,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios.This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ rule.Subsequently,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal features.Finally,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other models.The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

双馈风力发电系统机侧模型预测直接功率控制

提出一种基于瞬时功率理论的模型预测直接功率控制(DPC)策略,建立功率预测模型,分析了双馈式异步发电机(DFIG)转子侧变换器电压矢量对定子输出有功、无功功率变化影响。构造瞬时功率误差价值函数,以满足价值函数最小为目标计算下一时刻功率指令,利用功率预测模型和价值函数最小原则得出目标控制电压矢量,结合空间矢量脉宽调制(SVPWM)实现模型预测功率控制,同时将延时补偿引入功率预测模型中,减少谐波含量同时获得固定开关频率,具有良好的稳态性。仿真及实验结果表明了所提控制策略的可行性和有效性。

High performance decoupled active and reactive power control for three-phase grid-tied inverters using model predictive control

Finite control set-model predictive control (FCS-MPC) is employed in this paper to control the operation of a three-phase grid-connected string inverter based on a direct PQ control scheme. The main objective is to achieve high-performance decoupled control of the active and reactive powers injected to the grid from distributed energy resources (DER).The FCS-MPC scheme instantaneously searches for and applies the optimum inverter switching state that can achieve certain goals, such as minimum deviation between reference and actual power;so that both power components (P and Q) are well controlled to their reference values.In addition, an effective method to attenuate undesired cross coupling between the P and Q control loops, which occurs only during transient operation, is investigated. The proposed method is based on the variation of the weight factors of the terms of the FCS-MPC cost function, so a higher weight factor is assigned to the cost function term that is exposed to greater disturbance. Empirical formulae of optimum weight factors as functions of the reference active and reactive power signals are proposed and mathematically derived. The investigated FCS-MPC control scheme is incorporated with the LVRT function to support the grid voltage in fulfilling and accomplishing the up-to-date grid codes. The LVRT algorithm is based on a modification of the references of active and reactive powers as functions of the instantaneous grid voltage such that suitable values of P and Q are injected to the grid during voltage sag.The performance of the elaborated FCS-MPC PQ scheme is studied under various operating scenarios, including steady-state and transient conditions. Results demonstrate the validity and effectiveness of the proposed scheme with regard to the achievement of high-performance operation and quick response of grid-tied inverters during normal and fault modes.

Control-based Fault Current Limiter for Minimizing Impact of Distributed Generation Units on Protection Systems

Distributed generation units(DGUs)bring some problems to the existing protection system,such as those associated with protection blinding and sympathetic tripping.It is known that fault current limiters(FCLs)help minimize the negative impact of DGUs on the protection system.In this paper,a control-based FCL is proposed,i.e.,the FCL is integrated into the DGU control law.To this end,a predictive control strategy with fault current limitation is suggested.In this way,a DGU is controlled,not only for power exchange with the power grid but also to limit its fault current contribution.The proposal is posed as a constrained optimization problem allowing taking into account the current limit explicitly in the design process as a closed-loop solution.A linear approximation is proposed to cope with the inherent nonlinear constraints.The proposal does not require incorporating extra equipment or mechanisms in the control loop,making the design process simple.To evaluate the proposed control-based FCL,both protection blinding and sympathetic tripping scenarios are considered.The control confines the DGU currents within the constraints quickly,avoiding large transient peaks.Therefore,the impact on the protection system is reduced without the necessity that the DGU goes out of service.

Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world’s energy concerns in recent years.Photovoltaic(PV)technology is the fastest technique to convert solar radiation into electricity.Solar-powered buses,metros,and cars use PV technology.Such technologies are always evolving.Included in the parameters that need to be analysed and examined include PV capabilities,vehicle power requirements,utility patterns,acceleration and deceleration rates,and storage module type and capacity,among others.PVPG is intermit-tent and weather-dependent.Accurate forecasting and modelling of PV sys-tem output power are key to managing storage,delivery,and smart grids.With unparalleled data granularity,a data-driven system could better anticipate solar generation.Deep learning(DL)models have gained popularity due to their capacity to handle complex datasets and increase computing power.This article introduces the Galactic Swarm Optimization with Deep Belief Network(GSODBN-PPGF)model.The GSODBN-PPGF model predicts PV power production.The GSODBN-PPGF model normalises data using data scaling.DBN is used to forecast PV power output.The GSO algorithm boosts the DBN model’s predicted output.GSODBN-PPGF projected 0.002 after 40 h but observed 0.063.The GSODBN-PPGF model validation is compared to existing approaches.Simulations showed that the GSODBN-PPGF model outperformed recent techniques.It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.

A general analytical approach to reach maximum grid support by PMSG-based wind turbines under various grid faults

A novel fault ride-through strategy for wind turbines,based on permanent magnet synchronous generator,has been proposed.The proposed strategy analytically formulates the reference current signals,disregarding grid fault type and utilizes the whole system capacity to inject the reactive current required by grid codes and deliver maximum possible active power to support grid frequency and avoid generation loss.All this has been reached by taking the grid-side converter’s phase current limit into account.The strategy is compatible with different countries’grid codes and prevents pulsating active power injection,in an unbalanced grid condition.Model predictive current controller is applied to handling rapid transients.During faults,the energy storage system maintains DC-link voltage,which causes voltage fluctuations to be eliminated,significantly.A fault ride-through strategy was proposed for PMSG-based wind turbines,neglecting fault characteristics,second,reaching maximum possible grid support in faulty grid conditions,while avoiding over-current and third,considerable reduction in energy storage system size and power rating.Inspiring simulations have been carried out through MATLAB/SIMULINK to validate the feasibility and competency of the proposed fault ride-through method and efficiency of the entire control system.

Applied Energy

As interest in the distributed generation of solar power system in a building fa c,ade continues to increase,its technical performance(i.e.the amount of electricity generation)should be carefully investigated before its implementation.In this regard,this study aimed to develop the nine-node-based finite element model for estimating the technical performance of the distributed generation of solar power system in a building fa c,ade(FEM9-node),focusing on the improvement of the prediction performance.The developed model(FEM9-node)was proven to be superior to the four-node-based model(FEM4-node),which was developed in the previous study,in terms of both prediction accuracy and standard deviation.In other words,the prediction accuracy(3.55%)and standard deviation(2.93%)of the developed model(FEM9-node)was determined to be superior to those of the previous model(FEM4-node)(i.e.4.54%and 4.39%,respectively).The practical application was carried out to enable a decision maker(e.g.construction manager,facility manager)to understand how the developed model works in a clear way.It is expected that the developed model(FEM9-node)can be used in the early design phase in an easy way within a short time.In addition,it could be extended to any other countries in a global environment.

Impact of the cold surge event in January 2021 on the power system of China

During 6-10 January 2021,a recorded strong cold surge took place in China,with over 800 observational stations reaching their historical extremes.Unlike previous studies that focused on the response of either the power load or generation separately,this study quantitatively revealed the impacts on the balance between the demand and supply sides of the grid.On the demand side,the sensitivity of power load was found to increase substantially higher in southern China(0.533 GW°C^(−1))than in the northern region(0.139 GW°C^(−1))due to the limited municipal heating system.On the supply side,the hourly wind power generation dropped from the highest of 110 GW on 6 January to the lowest of 54 GW on 9 January due to the reduction in wind speed.In addition,a reduction in solar power generation was observed during 8-10 January.Thus,the balance of the power system was influenced by this cold event.As an effective adaptation measure,results further showed that early warning by three weeks ahead can be obtained by an operational climate model.The sensitivity of China's power system to such cold surge events may increase remarkably due to the expected increase in the proportion of wind and solar power generation in future new-type power systems.Thus,close cooperation between climate scientists and power engineers is needed to build the resilience of the power system to the cold extremes.

A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines

Predictive health monitoring of micro gas turbines can significantly increase the availability and reduce the operating and maintenance costs.Methods for predictive health monitoring are typically developed for large-scale gas turbines and have often focused on single systems.In an effort to enable fleet-level health monitoring of micro gas turbines,this work presents a novel data-driven approach for predicting system degradation over time.The approach utilises operational data from real installations and is not dependent on data from a reference system.The problem was solved in two steps by:1)estimating the degradation from time-dependent variables and 2)forecasting into the future using only running hours.Linear regression technique is employed both for the estimation and forecasting of degradation.The method was evaluated on five different systems and it is shown that the result is consistent(r>0.8)with an existing method that computes corrected values based on data from a reference system,and the forecasting had a similar performance as the estimation model using only running hours as an input.