## 1 Introduction  
As with statistics, the improvement of classical distributions is deemed as beneficial as many other worthwhile disciplines. With the addition of new factors, such as location, scale, or shape, these inferences were made. In recent years, statisticians introduced many new generalized classes of distributions. Reference [1] extended a two-parameter Weibull distribution to include a third variable. There was a rise in the adoption of [2] the straightforward technique of adding a single parameter to a family of distributions in recent years. As [3] showed, statistical distributions can be introduced using the cumulative distribution function (CDF) of an alpha power transformation approach. The cubic transmutation method was developed by [4] as a new way to generate distributions. Additionally, the researchers [5; 6; 7; 8] all used the transformation or to derive a new distribution, called the inverse or inverted distribution, with a domain of \((0,\infty)\). Furthermore, a distribution raised to a power of \(X=Y^{1/\beta}\) can be used to construct new probability distributions based on older distributions. In this manner, a wide range of valuable models can be created. There are numerous studies on transforming the power of various distributions. For example, [PERSON] et al. [9] presented a power Lindley distribution, the power Lomax distribution is offered by [10], and [PERSON] et al. [11] delivered a power transmuted inverse Rayleigh distribution. This work focuses on generating power Darna distributions by raising a Darna distribution to a positive power. To our knowledge, this is the first study to suggest power Darna distribution. In (2019), [PERSON] and [PERSON] [12] proposed a new lifetime model called Darna distribution (DD) and derived some of its properties and explained its applications in fitting some real data sets. The probability density function (pdf) of the DD is  
\[f_{DD}(y;\theta,\alpha)=\frac{\theta}{2\alpha^{2}+\theta^{2}}\bigg{(}2\alpha+ \frac{\theta^{4}y^{2}}{2\alpha^{3}}\bigg{)}e^{-\frac{\theta y}{\alpha}};\,y>0, \,\theta>0,\alpha>0, \tag{1}\]  
and the corresponding CDF is  
\[F_{DD}(y;\alpha,\theta)=1-\frac{\big{(}4\alpha^{4}+2\alpha^{2}\theta^{2}+ \theta^{4}y^{2}+2\alpha\theta^{3}y\big{)}}{2\alpha^{2}(2\alpha^{2}+\theta^{2} )}e^{-\frac{\theta y}{\alpha}};\,y>0,\,\alpha>0,\,\theta>0. \tag{2}\]  
The DD has \(r\)th moment of the form  
\[E(X^{r})=r!\Big{(}\frac{\alpha}{\theta}\Big{)}^{r}\bigg{(}\frac{4\alpha^{2}+( r+1)(r+2)\theta^{2}}{2(2\alpha^{2}+\theta^{2})}\bigg{)},\frac{\theta}{\alpha}>0,r=1,2,3,\ldots\]  
with reliability and hazard functions defined by  
\[R_{DD}(y;\theta,\alpha)=\frac{\big{(}4\alpha^{4}+2\alpha^{2}\theta^{2}+\theta ^{4}y^{2}+2\alpha\theta^{3}y\big{)}}{2\alpha^{2}(2\alpha^{2}+\theta^{2})}e^{ -\frac{\theta y}{\alpha}}, \tag{3}\]  
\[H_{DD}(y;\theta,\alpha)=\frac{4\alpha^{4}\theta+\theta^{5}y^{2}}{4\alpha^{5}+2 \alpha^{3}\theta^{2}+\alpha\theta^{4}y^{2}+2\alpha^{2}\theta^{3}y}. \tag{4}\]  
A growing number of statistical approaches are being developed to verify the validity of parametric distributions. There are a bunch of new efforts to address these issues. With reference to [PERSON] and [PERSON] estimators, [PERSON] and [PERSON] [13] and [PERSON] and [PERSON] [14] offered a new variety of the chi-squared test for the randomly censored real data. Then, for the accelerated loss distributions, [PERSON] et al. [15] assumed certain nonparametric adjustments for the Anderson-Darling statistic (ADS), the Kolmogorov-Smirnov statistic (KSS), and the Cramer-von Mises statistic (KVMS). For the proper censored data, a new chi-squared goodness-of-fit (GFT) test statistic was recently presented and devoted by [16]. For the distributional validation using right-censored approaches, the Bagdonavicius-Nikulin's (B-N) is the most chi-squared GFT test statistic used. For the right-censored validation, the (B-N) GFT test and a new adapted chi-square GFT test were used in this article to generate a censored maximum likelihood estimation (MLE).