Reinforced Deterministic and Probabilistic Load Forecasting via -Learning Dynamic Model Selection
Both deterministic and probabilistic load forecasting (DLF and PLF) are of critical importance
to reliable and economical power system operations. However, most of the widely used …
to reliable and economical power system operations. However, most of the widely used …
An adaptive optimization spiking neural P system for binary problems
M Zhu, Q Yang, J Dong, G Zhang, X Gou… - … Journal of Neural …, 2021 - World Scientific
Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to
directly derive an approximate solution of combinatorial problems with a specific reference …
directly derive an approximate solution of combinatorial problems with a specific reference …
A novel temporal feature selection based LSTM model for electrical short-term load forecasting
An accurate electrical Short-term Load Forecasting (STLF) is an eminent factor in the power
generation, electrical load dispatching and energy planning for the power supply …
generation, electrical load dispatching and energy planning for the power supply …
A Comprehensive Review on STATCOM: Paradigm of Modelling, Control, Stability, Optimal Location, Integration, Application, and Installation
The Static synchronous compensator (STATCOM) is a renowned FACTS (flexible alternating
current transmission system) device used in power grids to cope with protean conditions …
current transmission system) device used in power grids to cope with protean conditions …
Coordinated wide-area dam** control using deep neural networks and reinforcement learning
P Gupta, A Pal, V Vittal - IEEE Transactions on Power Systems, 2021 - ieeexplore.ieee.org
This paper proposes the design of two coordinated wide-area dam** controllers
(CWADCs) for dam** low frequency oscillations (LFOs), while accounting for the …
(CWADCs) for dam** low frequency oscillations (LFOs), while accounting for the …
Demand-side regulation provision from industrial loads integrated with solar PV panels and energy storage system for ancillary services
Nowadays, enabled by current smart grid technology, electricity consumers can play an
active role in providing ancillary service (AS) as a type of demand response. Participating …
active role in providing ancillary service (AS) as a type of demand response. Participating …
A data-driven approach for designing STATCOM additional dam** controller for wind farms
Due to the fluctuation characteristics of the wind turbine (WT) production, the design of the
control system needs to ensure the stability of the power system at different wind speeds. In …
control system needs to ensure the stability of the power system at different wind speeds. In …
A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration
With increasing proportion of wind energy in power systems, the intermittence of such
energy makes the system run a wide range of operating conditions. In this context, ordinary …
energy makes the system run a wide range of operating conditions. In this context, ordinary …
[HTML][HTML] Short-term electric load prediction using transfer learning with interval estimate adjustment
Although we are currently in the era of big data, it is always challenging to obtain complete
and large-scale data due to the information protection for users and enterprises. In most …
and large-scale data due to the information protection for users and enterprises. In most …
Optimal fractional-order tilted-integral-derivative controller for frequency stabilization in hybrid power system using salp swarm algorithm
Regulating the frequency fluctuations (known as load frequency control) in the power system
is an important aspect that directly signifies the balance between the generation and …
is an important aspect that directly signifies the balance between the generation and …